CN116150680B - Underground transportation junction damage state identification method and system based on big data - Google Patents
Underground transportation junction damage state identification method and system based on big data Download PDFInfo
- Publication number
- CN116150680B CN116150680B CN202310408873.5A CN202310408873A CN116150680B CN 116150680 B CN116150680 B CN 116150680B CN 202310408873 A CN202310408873 A CN 202310408873A CN 116150680 B CN116150680 B CN 116150680B
- Authority
- CN
- China
- Prior art keywords
- sample set
- training sample
- underground
- junction
- control index
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000006378 damage Effects 0.000 title claims abstract description 73
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012549 training Methods 0.000 claims abstract description 85
- 238000012544 monitoring process Methods 0.000 claims abstract description 54
- 238000010276 construction Methods 0.000 claims abstract description 46
- 238000012360 testing method Methods 0.000 claims abstract description 38
- 238000004088 simulation Methods 0.000 claims abstract description 31
- 238000004458 analytical method Methods 0.000 claims abstract description 30
- 238000004364 calculation method Methods 0.000 claims abstract description 25
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 24
- 238000005259 measurement Methods 0.000 claims abstract description 12
- 238000012986 modification Methods 0.000 claims description 9
- 230000004048 modification Effects 0.000 claims description 9
- 238000011161 development Methods 0.000 claims description 8
- 238000013461 design Methods 0.000 claims description 7
- 238000006073 displacement reaction Methods 0.000 claims description 7
- 230000005855 radiation Effects 0.000 claims description 7
- 208000027418 Wounds and injury Diseases 0.000 claims description 6
- 238000004141 dimensional analysis Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 208000014674 injury Diseases 0.000 claims description 6
- 230000002787 reinforcement Effects 0.000 claims description 5
- 238000003475 lamination Methods 0.000 claims description 4
- 238000010438 heat treatment Methods 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000009423 ventilation Methods 0.000 claims description 3
- 238000012916 structural analysis Methods 0.000 claims 1
- 239000010410 layer Substances 0.000 description 24
- 238000012546 transfer Methods 0.000 description 16
- 239000011229 interlayer Substances 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 3
- 230000000903 blocking effect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 229910000831 Steel Inorganic materials 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 239000010959 steel Substances 0.000 description 2
- 241000239290 Araneae Species 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 239000003673 groundwater Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
- 230000003313 weakening effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Computer Hardware Design (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Computer Security & Cryptography (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a system for identifying the damage state of an underground transportation junction based on big data, comprising the following steps: constructing an underground transportation junction construction scheme of the urban transportation complex; according to the construction scheme, digital simulation is carried out on the underground transportation junction of the urban transportation complex; load and boundary conditions are added for calculation and analysis, control index information of the feature points is extracted, and a training sample set is constructed; according to the numerical simulation analysis result, carrying out characteristic point monitoring layout on weak stress and weak deformation or key parts to obtain characteristic point monitoring data information, and selecting K characteristic point actual measurement structure control index monitoring values as a test sample set based on the characteristic point monitoring result; and carrying out underground junction structure damage identification and prediction according to all the control index information of the training sample set. Based on a big data training sample set, the invention provides an improved KNN algorithm by utilizing limited monitoring point data test samples, and realizes the identification and prediction of the damage state of the underground transportation junction.
Description
Technical Field
The invention relates to the technical field of urban traffic construction, comprehensive transportation junction or underground space construction, in particular to a method and a system for identifying the damage state of an underground transportation junction based on big data.
Background
With the rapid development of urban treatment in China, the contradiction between land resource shortage and traffic congestion in large and medium cities is more severe, and an intensive underground space integrating multi-line subways and municipal tunnels is constructed, so that the urgent need of opening the traffic bottleneck of urban center nodes and realizing sustainable development of urban living environments is met. The main technical problems to be solved are as follows:
(1) How to scientifically construct a multi-line three-dimensional traffic underground space of an urban center;
(2) How to ensure the safety of the whole life-time structure of a large deeply buried complex underground space;
(3) How to realize safe and green operation of the ultra-large passenger flow complex underground transportation junction.
Therefore, how to design an urban transportation junction meeting the requirements of multi-subway lines, multi-highway tunnels and circular lines for increasingly complex traffic environments, and how to realize the identification and prediction of the damage state of the urban transportation junction for complex underground transportation junction are the problems to be solved.
Disclosure of Invention
Aiming at the technical problems existing in the prior art, the invention provides a method and a system for identifying the damage state of an underground transportation junction based on big data. The invention constructs the construction scheme of the urban traffic complex underground transportation junction by taking three-ring lamination and multi-line radiation as basic forms and arranging the underground station hall of the overhead platform. The space structure form of a central node, a ring corridor and two-wing radiation corridor is created by adopting a 'same-direction merging and different-direction interchange' strategy, so that five-line intersection space layout with two municipal tunnels and three subway lines mutually crossed and separated is realized, and the whole traffic pattern is smooth and has no separation. Meanwhile, the recognition and prediction of the urban traffic complex underground transportation junction damage state are realized by improving the KNN algorithm.
According to a first aspect of the present invention, there is provided a method for identifying an underground transportation junction damage state based on big data, comprising the steps of:
step 1, constructing an underground transportation junction construction scheme of an urban transportation complex;
step 2, according to the construction scheme, digital simulation is carried out on the underground transportation junction of the urban transportation complex, load and boundary conditions are added for calculation and analysis, control index information of characteristic points is extracted, and a training sample set is constructed;
step 3, carrying out characteristic point monitoring layout on weak stress and weak deformation or key parts according to the numerical simulation analysis result; acquiring characteristic point monitoring data information; the control index information corresponding to the feature points is contained in the training sample set;
step 4, based on the characteristic point monitoring results, selecting K characteristic point actual measurement structure control index monitoring values as a test sample set;
and 5, finding out K nearest sample points in the training sample set based on the minimum distance, identifying and predicting the damage of the underground junction structure according to all control index information of the training sample set based on the condition that the training sample set where the K nearest neighbor sample points are located is regarded as the immediate state where the structure is located.
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, the method further comprises: and 6, programming the prediction method, associating a digital simulation platform, uploading monitoring point data in real time, automatically performing damage identification and evaluation by the program according to an algorithm model, visually monitoring, comparing the prediction development trend by combining an evolution curve, and performing real-time early warning according to a given rule for reference of safety guarantee decisions in a construction period and an operation period.
Optionally, the construction scheme of the underground transportation junction of the urban transportation complex comprises the following steps:
the construction scheme of the urban traffic complex underground transportation junction is constructed by taking three-ring lamination and multi-line radiation as basic forms and arranging the underground station hall of the overhead platform.
Optionally, the digital simulation of the urban traffic complex underground transportation hub comprises:
and building a BIM refined three-dimensional analysis model of the underground transportation junction by utilizing REVIT.
Optionally, the calculation analysis of the added load and boundary conditions comprises the following steps:
based on the created BIM structure model, a structure analysis model is generated, load and boundary condition information are added, calculation and analysis are carried out, structural member stress, strain, displacement, internal force and reinforcement information are generated, structural arrangement and size are optimized through finite element analysis, and the structural member stress, strain, displacement, internal force and reinforcement information are fed back to the BIM model for modification and adjustment.
Optionally, the method further comprises: establishing a multi-specialty collaborative work mechanism, synchronously linking the modified structure BIM model working set to a building and heating ventilation related specialty model working set through a shared data center set, performing collision check and engineering quantity statistics on the optimized BIM model, and importing the optimized and modified BIM model into finite element software again to perform three-dimensional numerical simulation calculation.
Optionally, the method further comprises: based on the optimized three-dimensional analysis model, finite element calculation is performed aiming at construction working conditions, design working conditions in an operation period and accidental working conditions possibly occurring, weak or key parts of stress and deformation are found out, parameterization analysis is performed on different load working conditions, and a stress and deformation performance evolution curve of the underground junction structure is formed.
Optionally, the identifying and predicting the damage of the underground junction structure according to all the control index information of the training sample set includes: the KNN algorithm is improved, the damage identification and the prediction of the underground junction structure are carried out according to all control index information of the training sample set, and the improved formula is as follows:
（1）
（2）
wherein M is the total number of various calculation working conditions in the training sample set; g is the total number of feature points in the training sample set; k is the total number of actually measured characteristic points in the test sample set;KNN distance measurement values of a test sample set and a training sample set containing K actually measured characteristic points are represented; />A control index information set which represents a test sample set corresponding to the K actually measured sample points; />The control index parameters corresponding to the working conditions of the training sample set j for representing the feature points k in the training sample set; />Is the positioning parameter of the feature points of the training set; />Representing positioning parameters of a test sample set and a training sample set corresponding to the K actually measured sample points; />Representing working condition parameters of a training sample set corresponding to the K actually measured sample points; />The working condition parameters of the test sample set corresponding to the K actually measured sample points are represented; />Represented as a training sample set operating mode parameter.
According to a second aspect of the present invention, there is provided an underground transportation junction damage status recognition system based on big data, comprising:
the construction scheme construction module is used for constructing an underground transportation junction construction scheme of the urban transportation complex;
the model building module is used for carrying out digital simulation on the underground transportation junction of the urban transportation complex according to the building scheme, adding load and boundary conditions for calculation and analysis, extracting control index information of the characteristic points and constructing a training sample set;
the data acquisition module is used for carrying out characteristic point monitoring layout on weak stress and weak deformation or key parts according to the numerical simulation analysis result and acquiring characteristic point monitoring data information, wherein control index information corresponding to the characteristic points is contained in a training sample set;
the damage prediction module is used for selecting K actually-measured structure control index monitoring values of the characteristic points as a test sample set based on the characteristic point monitoring results; and finding out K nearest sample points in the training sample set based on the minimum distance, recognizing and predicting the damage of the underground junction structure according to all control index information of the training sample set based on the condition that the working condition of the training sample set where the K nearest neighbor sample points are is regarded as the immediate state of the structure.
The automatic early warning module is used for programming the prediction method, associating the digital simulation platform, uploading the monitoring point data in real time, automatically carrying out damage identification evaluation on the program according to an algorithm model, visually monitoring, comparing the prediction development trend by combining an evolution curve, and carrying out early warning in real time according to a given rule for reference of safety guarantee decisions in a construction period and an operation period.
The invention has the technical effects and advantages that:
the invention provides a method and a system for identifying the damage state of an underground transportation junction based on big data, which are used for constructing an underground transportation junction construction scheme of an urban transportation complex by taking three-ring lamination and multi-line radiation as basic forms and arranging an overhead platform underground station hall. The space structure form of a central node, a ring corridor and two-wing radiation corridor is created by adopting a 'same-direction merging and different-direction interchange' strategy, so that five-line intersection space layout with two municipal tunnels and three subway lines mutually crossed and separated is realized, and the whole traffic pattern is smooth and has no separation.
The construction scheme of the underground transportation junction of the urban transportation complex is digitally simulated by combining BIM and finite element simulation interaction technology, iterative optimization is designed, three-dimensional visualization construction is carried out, a subway platform and a highway tunnel are built above the middle part of a hall together, a unique brand new construction pattern of an overhead station platform and a highway tunnel in a large underground space is ingeniously created, the ingenious utilization of the underground space is realized, a smooth and unblocked large underground transportation space is built, a three-dimensional layout scheme for realizing intersection of five transportation lines in a three-layer underground space is created, the problems that the design is puzzled with three-line transfer streamline interweaving and transfer space of an operator are separated by lines are effectively solved, the optimal transportation function is ensured, meanwhile, compared with the conventional solution, two-layer underground structures are saved, engineering depth and investment are greatly saved, and underground space resources are utilized about and efficiently.
Meanwhile, based on a big data training sample set, a limited monitoring point data test sample is utilized, an improved KNN algorithm is provided, and recognition and prediction of the urban traffic complex underground transportation junction damage state are realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
FIG. 1 is a flowchart of steps of a method for identifying damage status of underground transportation hubs based on big data according to an embodiment of the present invention;
FIG. 2 is a schematic view of an underground transportation junction of an urban transportation complex according to an embodiment of the present invention;
FIG. 3 is a diagram of a digital simulation and simulation architecture of a BIM of an underground transportation junction according to an embodiment of the present invention;
FIG. 4 is a graph showing the variation of the lower column sedimentation value along the lateral direction for different formation resistances provided by an embodiment of the present invention;
FIG. 5 is a schematic illustration of a section 1 strain sensor station arrangement provided by an embodiment of the present invention;
fig. 6 is a schematic diagram of an underground transportation junction damage status recognition system based on big data according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The method provided by the invention can be implemented in a terminal environment, wherein the terminal can comprise one or more of the following components: processor, storage medium, and display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
It can be appreciated that, based on the defects in the background technology, the embodiment of the invention provides a method for identifying the damage state of an underground transportation junction based on big data, as shown in fig. 1, the method comprises the following steps:
step 1: constructing an underground transportation hub construction scheme of the urban transportation complex;
in the embodiment of the invention, as shown in fig. 2 in particular, the construction scheme of the urban traffic complex underground transportation junction according to the embodiment of the invention adopts five-layer design, including an underground layer, an underground interlayer, an underground first layer, an underground second layer and an underground third layer; wherein, the liquid crystal display device comprises a liquid crystal display device,
the underground three layers are provided with second subway lines, and the stations of the second subway lines are provided with second subway stations, so that the passing requirements of the second subway stations and the sections are met;
the underground two-layer is provided with a first subway line and a second highway tunnel, a first subway platform is arranged at the first subway line platform, an up-down connection channel is arranged between the first subway platform and the second subway platform, and the connection channel comprises a lifting elevator or a stair;
a transfer hall, a corridor for connecting the transfer hall and a third subway line transfer channel are arranged on the underground layer, an up-down connection channel is arranged between the transfer hall and the first subway platform, and the connection channel comprises a lifting elevator or a stair and is used for passing pedestrians;
the underground interlayer is provided with a first highway tunnel, a third subway platform, a third subway line and a non-motor vehicle loop; the first highway tunnel and the first subway line platform are consistent in running direction and are arranged side by side, passengers enter the first underground layer (a transfer hall and a third subway line transfer passage) and enter the second underground layer (the first subway line platform and the second highway tunnel) and enter the first subway line platform for riding through a non-motor vehicle loop. The non-motor vehicle loop is used for passengers and the non-motor vehicles to cross the street, and the first highway tunnel is used for highway traffic along the north-south direction;
the ground layer is provided with ground traffic and a rotary island, and the rotary island is provided with a large sculpture;
in order to facilitate the passersby to pass up and down, streamline transfer stairs are respectively arranged among an underground three-layer (a second subway line platform), an underground two-layer (a second highway tunnel and a third subway line south line extension section), an underground one-layer (a transfer hall and a third subway line transfer passage) and an underground one-layer interlayer (a first subway line platform, a first highway tunnel and a non-motor vehicle loop).
In the embodiment, the outline of the engineering main body junction area of the urban traffic complex underground traffic junction construction scheme is circular, column nets are arranged along the circumferential direction and the radial direction according to the main passenger flow advancing direction, the circumferential frame column positions are strictly ensured, and the blocking of the circumferential cross-street passenger flow is avoided; the engineering scale is larger, and the underground one-layer column net adopts a large-span structural form, so that the space effect and the comfort level are improved.
In the embodiment, the underground transportation junction building scheme of the urban transportation complex is characterized in that a round shared hall extends to a sinking courtyard of each land, and along a two-wing radiation corridor, a subway platform and a municipal road tunnel are lifted to an underground one-layer interlayer, the whole underground one-layer hall is arranged to be a through subway transfer and transportation layer, and a through and unblocked underground one-layer transfer hall is formed by a unique space structure mode, so that a main flow line of a passenger flow is unobstructed; the special layout of the overhead platform of the underground station hall is formed, the problem of separation of subway lines to the transfer hall is solved, and meanwhile, the space sense is greatly improved.
Step 2: according to the construction scheme, digital simulation is carried out on the underground transportation junction of the urban transportation complex, load and boundary conditions are added for calculation and analysis, control index information of characteristic points is extracted, and a training sample set is constructed;
in the embodiment of the invention, the digital simulation of the construction scheme of the urban traffic complex underground transportation junction comprises the following steps: and building a BIM refined three-dimensional analysis model of the underground transportation junction by utilizing REVIT.
The BIM refined three-dimensional model comprises a foundation pit supporting structure model and a main body structure model. The foundation pit supporting structure model comprises guard piles (walls), crown beams, supports, retaining walls, temporary upright posts and the like; the main structural model comprises structural plates, upright posts, side walls, longitudinal beams and Liang Jiaye, and is specifically referred to as shown in fig. 3.
The calculation and analysis of the added load and the boundary condition comprise the following steps:
based on the created BIM structure model, the structure analysis software is imported through the interface software to generate a structure analysis model, and after information such as load, boundary conditions and the like is added, calculation and analysis are performed to generate stress, strain, displacement, internal force and reinforcement information of the structural member.
Further, structural arrangement and size are optimized through finite element analysis, and the structural arrangement and size are fed back to the BIM model for modification and adjustment. Such as: and carrying out three-dimensional integral stress and deformation analysis on the foundation pit supporting structure by using deep foundation pit calculation software, and adjusting and optimizing the size and supporting arrangement of the enclosure structure. And carrying out structural integral stress analysis by using MIDAS/GEN, and carrying out component reinforcement calculation according to an internal force analysis result. And checking the MIDAS/GEN calculation result by using PKPM software, so as to ensure the accuracy of structural stress calculation.
And then further comprises: establishing a multi-specialty collaborative work mechanism, synchronously linking the modified structure BIM model working set to a building, heating and ventilation related specialty model working set through a shared data center set, and carrying out collision check and engineering quantity statistics on the optimized BIM model. And (3) importing the optimized and modified BIM model into finite element software again to perform three-dimensional numerical simulation calculation.
The process can be circularly iterated for a plurality of times until the reasonable and simple structural arrangement is optimized, a circular+radial column net system and a spider web type ring beam centripetal radial large-span structural system are formed, and blocking of structural members to circular street-crossing passenger flows and shielding to large space vision are avoided to the greatest extent; the space between the main beams is skillfully utilized to provide laying conditions for the main equipment pipelines which are radially arranged, so that the net height of the underground space is saved; meanwhile, by combining the favorable conditions of the overhead platform, the area except the overhead platform is set as a high-traffic hall, so that the sense of depression of a large space and low clearance is effectively avoided.
Based on the optimized three-dimensional analysis model, carrying out finite element calculation aiming at construction working conditions, design working conditions in an operation period and possible accidental working conditions, and judging the situation that the settlement value of a lower row column is changed along the transverse direction under different formation resistance by referring to the figure 4, such as weakening of formation resistance or partial loss (such as karst); and (3) unexpectedly stacking, peripheral construction, groundwater change and the like, finding out weak or key parts of stress and deformation, and carrying out parameterization analysis on different load working conditions to form a stress and deformation performance evolution curve of the underground junction structure.
The step of extracting control index information of the feature points and the step of constructing a training sample set comprises the following steps:
according to the formed stress and deformation performance evolution curve of the underground junction structure, 6 key control indexes such as the horizontal displacement q of the enclosing structure are provided 1 Width q of concrete crack 2 Strain q of concrete 3 Stress q of steel bar 4 Layer displacement angle q 5 Deflection q of flexural member 6 . Extracting control index information of feature points(i is a characteristic point positioning parameter number, i=1, 2, & gt, the maximum value of N, N is the total number of three-dimensional model nodes, j is a characteristic point working condition parameter number, j=1, 2, & gt, the maximum value of M is the total number of various computing working conditions;z=1, 2,3,4,5,6, … denotes a control index type number), wherein ∈>Positioning parameters for feature points, < >>Is a characteristic point working condition parameter. By->And constructing a KNN algorithm training sample set Q. The data of the training sample set is increased, and the accuracy of damage prediction can be improved.
Step 3, carrying out characteristic point monitoring layout on weak stress and weak deformation or key parts according to the numerical simulation analysis result; acquiring characteristic point monitoring data information, wherein control index information corresponding to the characteristic points is contained in a KNN training sample set Q;
the feature point monitoring layout for the weak stress and weak deformation or key parts comprises the following steps:
according to the numerical simulation analysis result, characteristic point monitoring points are distributed on weak or key parts of stress and deformation, and particularly as shown in fig. 5, strain sensors are distributed in complex areas of a structural system, such as a rigidity mutation area, a structure discontinuous area and the like; structural strength weak areas, such as stress peak areas, bearing capacity minimum areas and the like; complicated surrounding areas, such as poor geological areas, areas where geological conditions change, etc.
In the embodiment of the invention, the total number of the monitored characteristic points is denoted as P, and the monitoring or detection data information of the characteristic points P (p=1, 2,..p) is obtained through continuous monitoring in the construction period and the operation period. Note that the control index information obtained by the numerical simulation calculation corresponding to the feature point pShould be included in the KNN training sample set Q.
Step 4, based on the characteristic point monitoring results, selecting K characteristic point actual measurement structure control index monitoring values as a test sample set;
specifically, the method comprises the following steps: based on specialThe monitoring result of the feature points is that the monitoring value of the control index of the actually measured structure of K feature points is selected as a test sample set(i is a feature point positioning parameter number, i=1, 2,..k), finding K nearest sample points in the training sample set Q based on the minimum distance Lp, and then based on the working conditions of the training sample set where the K nearest sample points are located>Regarding the instant state of the structure, corresponding to the working condition of the training sample set +.>All control index information（/>And (3) judging and predicting the damage of the underground junction structure according to the working condition of the training sample set selected for the KNN neighbor, wherein i=1, 2,..G, and G is the total number of the feature points in the training sample set Q).
It should be noted that, the K value may be set empirically as the test sample set.
And 5, finding out K nearest sample points in the training sample set based on the minimum distance, identifying and predicting the damage of the underground junction structure according to all control index information of the training sample set based on the condition that the training sample set where the K nearest neighbor sample points are located is regarded as the immediate state where the structure is located.
The step of carrying out underground junction structure damage identification and prediction according to all the control index information of the training sample set comprises the following steps: the KNN algorithm is improved, the damage identification and the prediction of the underground junction structure are carried out according to all control index information of the training sample set, and the improved KNN discrimination algorithm has the following formula:
（1）
（2）
wherein M is the total number of various calculation working conditions in the training sample set; g is the total number of feature points in the training sample set; k is the total number of actually measured characteristic points in the test sample set; z is the control index type corresponding to the actual measurement characteristic point sample set, such as the horizontal displacement of the building envelope, the width of the concrete crack, the concrete strain and the steel bar stress.
KNN distance measurement values of a test sample set and a training sample set containing K actually measured characteristic points are represented; />The test sample set and the training sample set positioning parameters corresponding to the K actual measurement sample points are represented (the control index information corresponding to the actual measurement characteristic points is contained in the training sample set based on the assumption, so that the test sample set and the training sample set positioning parameters corresponding to the K actual measurement sample points are the same); />Representing working condition parameters of a training sample set corresponding to the K actually measured sample points; />And the working condition parameters of the test sample set corresponding to the K actually measured sample points are represented.
Representing the feature point k in the training sample set (training sample set positioning parameter is +.>) Is set j (training sample set operating parameter is +.>) The corresponding control index parameters; />Represents the measured feature point k in the test sample set (based on the assumption that the test sample set positioning parameters are the same as the training sample set positioning parameters, take +.>) Is set j (test sample set operating parameter is +.>) Corresponding control index parameters; />Is the positioning parameter of the feature points of the training set; j is a training sample set working condition number which is closest to the test sample set and found by a KNN algorithm;the training sample set working condition parameters corresponding to the training sample set working condition number J closest to the test sample set are found through a KNN algorithm; />A control index information set which represents a test sample set corresponding to the K actually measured sample points; />And (5) representing a training sample set control index information set which is closest to the test sample set and is found by the KNN algorithm.
The damage state identification method can predict the damage state of the point positions of the non-monitoring section based on the monitoring data collected by the limited monitoring section, and can realize the identification and prediction of the overall damage state of the underground junction structure.
For easy understanding, the embodiment of the invention takes the concrete compressive strain discrimination structure damage state as an example for carrying out exemplary explanation, and the concrete method comprises the following steps:
(1) The damage factor of the concrete is basically 0 before the concrete reaches the peak strength concrete compressive strain; when the damage factor of the concrete reaches 0.7, the concrete is considered to be coagulatedThe soil is completely destroyed and the bearing capacity is lost. The damage factor of the concrete is basically linear with compressive strain when the damage factor is between 0 and 0.7, and can be expressed by a function:wherein a and b are coefficients related to concrete grade; />Is the compressive strain of the concrete.
And measuring the concrete strain through a pre-buried strain sensor in the concrete, and calculating to obtain an actual measurement concrete damage value. The injury factors are classified into 1,2,3,4 and 5 grades, and different grades correspond to different concrete injury factors. The index of the injury factor grading refers to table 1 below.
TABLE 1 injury factor rankings
| Grade index | 1 | 2 | 3 | 4 | 5 |
| Concrete injury factor D | 0 | 0<D＜0.1 | 0.1≤D＜0.2 | 0.2≤D＜0.5 | 0.5≤D |
(2) Based on the monitoring result, an improved KNN discrimination algorithm is adopted to judge the working condition of the training sample set closest to the damage result, and the structural damage condition is judged according to the structural damage result under the working condition.
Examples: and aiming at numerical simulation calculation working conditions of large-scale stacking, arranging damage positions of structures under different stacking conditions as shown in the following table 2. In the spatial coordinates, the decimal point followed by the numbers 1, 5, 9 represent the bottom, middle and top of the component, respectively. The 22 characteristic points in the table below in the construction period are all undamaged; when the stacking load is 70kPa, 22 characteristic points are damaged. The concrete strain gauge is embedded in 8 characteristic points, an improved KNN discrimination algorithm is adopted in combination with the monitoring results of the 8 concrete strain gauges, at present, the monitoring results of the 8 characteristic point concrete strain gauges are all within design values, the monitoring results are not damaged, and the structural damage value lp=0.000 in the construction period is closest to the structural damage value lp=0.000 in the construction period in combination with the KNN improved algorithm, namely the current damage condition of the structure is in the construction period.
In this example, m= 9,G =22, k= 8,Z is the control index type number concrete strain,see table 2 for the last line L p And calculating a result.
TABLE 2 statistical table of damage condition of structural feature points under different working conditions
Step 6: the method is programmed, the BIM platform is associated, the monitoring point data is uploaded in real time, the program automatically carries out damage identification and evaluation according to the algorithm model, visual monitoring is carried out, the evolution curve is combined to compare and predict the development trend, real-time early warning can be carried out according to a given rule, and the safety guarantee decision reference in the construction period and the operation period is provided.
In summary, the invention provides a method for identifying the damage state of an underground transportation junction based on big data, researches the spatial combination types of various transportation lines, provides a brand-new building type based on homodromous merging, different-direction overpass and underground transportation junction, innovates a brand-new method for identifying the damage state of the transportation junction based on the brand-new building type, improves a KNN algorithm and realizes intelligent identification prediction of the damage state of the underground transportation junction. The underground transportation space without blocking is smooth, the difficulty that the multi-line transfer streamline interweaving and transfer space puzzling the subway operation is separated by lines is thoroughly solved, and meanwhile, compared with the conventional solving method, the underground transportation space has the advantages of reducing the number of layers of an underground structure and saving investment.
In addition, the embodiment of the invention also provides a system for identifying the damage state of the underground transportation junction based on big data, which is applied to the identification method, and particularly as shown in fig. 6, the identification system comprises:
the construction scheme construction module is used for constructing an underground transportation junction construction scheme of the urban transportation complex;
the model building module is used for carrying out digital simulation on the underground transportation junction of the urban transportation complex according to the building scheme, adding load and boundary conditions for calculation and analysis, extracting control index information of the characteristic points and constructing a training sample set;
the data acquisition module monitors and lays characteristic points of weak stress and weak deformation or key parts according to the numerical simulation analysis result; the method comprises the steps of acquiring characteristic point monitoring data information, wherein control index information corresponding to characteristic points is contained in a training sample set;
the damage prediction module is used for selecting K actually-measured structure control index monitoring values of the characteristic points as a test sample set based on the characteristic point monitoring results; and finding out K nearest sample points in the training sample set based on the minimum distance, recognizing and predicting the damage of the underground junction structure according to all control index information of the training sample set based on the condition that the working condition of the training sample set where the K nearest neighbor sample points are is regarded as the immediate state of the structure.
The automatic early warning module is used for programming the prediction method, associating the digital simulation platform, uploading the monitoring point data in real time, automatically carrying out damage identification evaluation on the program according to an algorithm model, visually monitoring, comparing the prediction development trend by combining an evolution curve, and carrying out early warning in real time according to a given rule for reference of safety guarantee decisions in a construction period and an operation period.
It can be understood that the underground transportation junction damage status recognition system based on big data provided by the present invention corresponds to the underground transportation junction damage status recognition method based on big data provided in the foregoing embodiments, and the relevant technical features of the underground transportation junction damage status recognition system based on big data may refer to the relevant technical features of the underground transportation junction damage status recognition method based on big data, which are not described herein again.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (6)
1. The underground transportation junction damage state identification method based on big data is characterized by comprising the following steps of:
step 1, constructing an underground transportation junction construction scheme of an urban transportation complex;
step 2, according to the construction scheme, BIM digital simulation is carried out on the underground transportation junction of the urban transportation complex, a structural analysis model is generated based on the created BIM structural model, load and boundary condition information are added, calculation analysis is carried out, structural member stress, strain, displacement, internal force and reinforcement information are generated, structural arrangement and size are optimized through finite element analysis, and the structural arrangement and size are fed back to the BIM model for modification and adjustment;
establishing a multi-specialty collaborative work mechanism, synchronously linking a modified structure BIM model working set to a building and heating ventilation related specialty model working set through a shared data center set, performing collision check and engineering quantity statistics on an optimized BIM model, and importing the optimized and modified BIM model into finite element software again to perform three-dimensional numerical simulation calculation;
based on the optimized three-dimensional analysis model, carrying out finite element calculation aiming at construction working conditions, design working conditions in an operation period and possibly happening accidental working conditions, finding out weak or key parts of stress and deformation, and carrying out parameterization analysis on different load working conditions to form an evolution curve of stress and deformation performance of the underground junction structure; extracting control index information of the feature points, and constructing a training sample set;
step 3, carrying out characteristic point monitoring layout on weak stress and weak deformation or key parts according to the numerical simulation analysis result; acquiring characteristic point monitoring data information; the control index information corresponding to the feature points is contained in the training sample set;
step 4, based on the characteristic point monitoring results, selecting K characteristic point actual measurement structure control index monitoring values as a test sample set;
and 5, finding out K nearest sample points in the training sample set based on the minimum distance, improving a KNN algorithm based on the real-time state of the structure where the working condition of the training sample set where the K nearest sample points are located is regarded as, and carrying out damage identification and prediction on the underground transportation junction structure according to all control index information of the training sample set, wherein the improved KNN algorithm has the formula:
wherein M is the total number of various calculation working conditions in the training sample set; g is the total number of feature points in the training sample set; k is the total number of actually measured characteristic points in the test sample set; l (L) p (x K ,y K -Y K ) KNN distance measurement values of a test sample set and a training sample set containing K actually measured characteristic points are represented; q (x) K ，y K ) A control index information set which represents a test sample set corresponding to the K actually measured sample points; q Z (x k ,y kj ) The control index parameters corresponding to the working conditions of the training sample set j for representing the feature points k in the training sample set; x is x i Is the positioning parameter of the feature points of the training set; x is x K Representing positioning parameters of a test sample set and a training sample set corresponding to the K actually measured sample points; y is K Representing working condition parameters of a training sample set corresponding to the K actually measured sample points; y is Y K The working condition parameters of the test sample set corresponding to the K actually measured sample points are represented; y is kj Expressed as working condition parameters of training sample set, q Z (x k ,Y kj ) The control index parameters corresponding to the working conditions of the test sample set j for expressing the actually measured characteristic points k in the test sample set; y is Y kj Working condition parameters of a test sample set;the training sample set working condition parameters corresponding to the training sample set working condition number J closest to the test sample set are found through a KNN algorithm; />And (5) representing a training sample set control index information set which is closest to the test sample set and is found by the KNN algorithm.
2. The method for identifying the damage state of the underground transportation junction based on big data according to claim 1, further comprising:
and 6, programming the identification method, associating a digital simulation platform, uploading monitoring point data in real time, automatically performing damage identification and evaluation by the program according to an algorithm model, visually monitoring, comparing and predicting development trend by combining an evolution curve, and performing real-time early warning according to a given rule for reference of safety guarantee decisions in a construction period and an operation period.
3. The method for identifying the damage state of an underground transportation junction based on big data according to claim 1, wherein the constructing the underground transportation junction construction scheme of the urban transportation complex comprises:
the construction scheme of the urban traffic complex underground transportation junction is constructed by taking three-ring lamination and multi-line radiation as basic forms and arranging the underground station hall of the overhead platform.
4. The method for recognizing damage status of underground transportation junction based on big data according to claim 1, wherein the digital simulation of the underground transportation junction of the urban transportation complex comprises:
and building a BIM three-dimensional analysis model of the underground transportation junction by utilizing REVIT.
5. An underground transportation junction damage state recognition system based on big data, which is applied to the recognition method of claim 1, wherein the recognition system comprises:
the construction scheme construction module is used for constructing an underground transportation junction construction scheme of the urban transportation complex;
the model building module is used for carrying out digital simulation on the underground transportation junction of the urban transportation complex according to the building scheme, adding load and boundary conditions for calculation and analysis, extracting control index information of the characteristic points and constructing a training sample set;
the data acquisition module is used for carrying out characteristic point monitoring layout on weak stress and weak deformation or key parts according to the numerical simulation analysis result to acquire characteristic point monitoring data information, wherein control index information corresponding to the characteristic points is contained in a training sample set;
the damage prediction module is used for selecting K actually-measured structure control index monitoring values of the characteristic points as a test sample set based on the characteristic point monitoring results; and finding out K nearest sample points in the training sample set based on the minimum distance, recognizing and predicting the damage of the underground transportation junction structure according to all control index information of the training sample set based on the condition of the training sample set where the K nearest neighbor sample points are located as the immediate state where the structure is located.
6. The underground transportation hub injury status identification system of claim 5 further comprising:
the automatic early warning module is used for programming the identification method, associating the digital simulation platform, uploading the monitoring point data in real time, automatically carrying out damage identification evaluation according to an algorithm model by the program, visually monitoring, comparing and predicting the development trend by combining an evolution curve, and carrying out early warning in real time according to a given rule for reference of safety guarantee decisions in the construction period and the operation period.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310408873.5A CN116150680B (en) | 2023-04-18 | 2023-04-18 | Underground transportation junction damage state identification method and system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310408873.5A CN116150680B (en) | 2023-04-18 | 2023-04-18 | Underground transportation junction damage state identification method and system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116150680A CN116150680A (en) | 2023-05-23 |
| CN116150680B true CN116150680B (en) | 2023-07-21 |
Family
ID=86358481
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310408873.5A Active CN116150680B (en) | 2023-04-18 | 2023-04-18 | Underground transportation junction damage state identification method and system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116150680B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117195664B (en) * | 2023-11-07 | 2024-01-12 | 北京市科学技术研究院 | Underground pipeline monitoring and early warning method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200004A (en) * | 2014-07-28 | 2014-12-10 | 东北大学 | Optimized bridge damage identification method based on neural network |
| EP3555846A1 (en) * | 2016-12-13 | 2019-10-23 | Koninklijke Philips N.V. | Stress prediction and stress assessment for device insertion into a deformable object |
| CN113553749A (en) * | 2021-09-22 | 2021-10-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Bridge health monitoring method, system, computer and storage medium |
| CN113868904A (en) * | 2021-09-01 | 2021-12-31 | 浙江大学 | Method for automatically generating existing structure finite element model based on three-dimensional point cloud |
| WO2022047970A1 (en) * | 2020-09-01 | 2022-03-10 | 浙江勤业建工集团有限公司 | Bim-based method for simulated calculation of deep foundation pit before construction |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8494827B2 (en) * | 2009-09-25 | 2013-07-23 | Exxonmobil Upstream Research Company | Method of predicting natural fractures and damage in a subsurface region |
| US10796394B2 (en) * | 2017-05-16 | 2020-10-06 | One Concern, Inc. | Estimation of damage prevention with building retrofit |
| WO2018223194A1 (en) * | 2017-06-09 | 2018-12-13 | Alerte Digital Sport Pty Ltd | Systems and methods of prediction of injury risk with a training regime |
| CN111307092A (en) * | 2019-12-18 | 2020-06-19 | 华中科技大学 | Automatic continuous monitoring system and information platform for subway deep tunnel engineering construction safety |
| CN112528365B (en) * | 2020-11-30 | 2022-10-14 | 北京航空航天大学 | Method for predicting healthy evolution trend of underground infrastructure structure |
| CN114417537B (en) * | 2022-04-01 | 2022-06-17 | 中国工程物理研究院流体物理研究所 | Open type walking framework structure deformation field real-time measurement method, device and system |
-
2023
- 2023-04-18 CN CN202310408873.5A patent/CN116150680B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200004A (en) * | 2014-07-28 | 2014-12-10 | 东北大学 | Optimized bridge damage identification method based on neural network |
| EP3555846A1 (en) * | 2016-12-13 | 2019-10-23 | Koninklijke Philips N.V. | Stress prediction and stress assessment for device insertion into a deformable object |
| WO2022047970A1 (en) * | 2020-09-01 | 2022-03-10 | 浙江勤业建工集团有限公司 | Bim-based method for simulated calculation of deep foundation pit before construction |
| CN113868904A (en) * | 2021-09-01 | 2021-12-31 | 浙江大学 | Method for automatically generating existing structure finite element model based on three-dimensional point cloud |
| CN113553749A (en) * | 2021-09-22 | 2021-10-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Bridge health monitoring method, system, computer and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 二七长江大桥健康监测***研究;陈国刚;中外公路;第34卷(第01期);第170-174页 * |
| 基于马氏距离及K最近邻算法的结构优化设计;曹鸿猷等;武汉理工大学学报;第44卷(第10期);第60-71页 * |
| 大型地铁十字换乘车站施工阶段变形预测;邓治纲等;岩石力学与工程学报;第27卷(第S1期);第2929-2936页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116150680A (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lin et al. | Novel model for risk identification during karst excavation | |
| CN105387274B (en) | A kind of underground utilities repair householder method, apparatus and system | |
| CN110956335B (en) | Road safety evaluation system and method based on forecast accident rate grading | |
| CN116150680B (en) | Underground transportation junction damage state identification method and system based on big data | |
| CN113779835A (en) | AI and intelligent monitoring system based deep and large foundation pit safety early warning method | |
| Guo et al. | Multi-source information fusion for safety risk assessment in underground tunnels | |
| CN109767042A (en) | Works of service team for civil air defence's site selecting method based on land used assessment | |
| CN102384856A (en) | Probabilistic finite element method (PFEM)-based steel-bridge fatigue reliability evaluation method | |
| CN111042143A (en) | Foundation pit engineering early warning method and system based on analysis of large amount of monitoring data | |
| Malinowska | A fuzzy inference-based approach for building damage risk assessment on mining terrains | |
| CN105718658A (en) | Large-size bridge online evaluating system | |
| Yi et al. | A mixed-integer linear programming approach for temporary haul road design in rough-grading projects | |
| CN110619187A (en) | Concrete dam construction scheme and resource conflict detection and adjustment method | |
| CN114418333A (en) | Multi-factor comprehensive weighting road route scheme evaluation method | |
| CN115700666A (en) | Intelligent comprehensive evaluation method for maintenance and investigation of mountain highway subgrade | |
| CN113221399B (en) | Decision method for dividing scheme of railway tunnel fabricated lining components | |
| CN114997671A (en) | Foundation pit deformation safety risk assessment method based on artificial neural network and entropy method | |
| CN110084460A (en) | A kind of quantization assessment method of obstacle free airspace condition | |
| Lai et al. | Review of constraints and critical success factors of developing urban underground space | |
| CN112785141A (en) | Comprehensive pipe gallery whole life cycle planning design intrinsic safety risk assessment method | |
| CN110569598A (en) | method for predicting maximum waterlogging draining capability of rainwater pipeline and road combined system | |
| CN115481944A (en) | Engineering waste slag position determination method | |
| Herabat et al. | A decision support system for flexible pavement treatment selection | |
| Hasan | Decision support system for crane selection and location optimization on construction sites | |
| JP4252606B2 (en) | Priority evaluation system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |