CN112052624A - Method for estimating and predicting bridge life based on big data - Google Patents
Method for estimating and predicting bridge life based on big data Download PDFInfo
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Abstract
The invention discloses a method for estimating and predicting the service life of a bridge based on big data, which comprises the following steps: establishing a bridge service life evaluation prediction model based on big data; applying the bridge service life evaluation prediction model to a big data platform facing a target user; inputting characteristic parameters of a target bridge at the big data platform by a target user; the bridge life evaluation prediction model compares the characteristic parameters of the target bridge with the characteristic parameters of the known bridges in the database, and finds out N groups of the closest characteristic parameters and the bridge life; and calculating the service life M of the target bridge according to the weight of the service life of the bridge influenced by the characteristic parameters. The bridge service life detection in the traditional industry and the big data processing of the high and new technology are fused with each other, and a bridge service life assessment and prediction model based on the big data is established, so that the real service life of the bridge can be predicted accurately, the cost of manpower and material resources is greatly reduced, and potential safety hazards are reduced.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method for estimating and predicting the service life of a bridge based on big data.
Background
With the continuous development of economy and science and technology, part of core technologies in the information field realize innovation and breakthrough, basic general technologies such as integrated circuits and operating systems are accelerated to catch up, advanced technical researches such as artificial intelligence, big data, cloud computing and the Internet of things are accelerated, and quantum communication, high-performance computing and the like make major breakthrough.
The digital economy expands a new economic development space, new technology, new state and new mode are continuously developed, the shared economy is developed vigorously, the scale of network retail and mobile payment transaction is the first world, and the scale of the digital economy is the second world. Big data play an important role in the modern development process, go to reality from the dream, change and enrich people's life: opening the navigation to strange places as if the navigation is carried with thousands of miles; the toll station easily passes through mobile payment; a mobile phone is carried on the belt, and the person can enjoy the game. With the rapid development of electronic information technology, digitization and networking have been advanced into various fields of social life, and become indispensable parts in daily work and life of people.
However, there are many semi-permanent bridges and temporary bridges in the road bridge, the load-bearing capacity of these bridges is generally lower than that of normal bridges, it is difficult to resist natural disasters, it is difficult to meet the current traffic requirements, the safety hazard is great, and these bridges have no clear identification, and there is no related awareness of the passing vehicles. As a country with thousands of bridges, it is obviously very labor and material consuming to overhaul each bridge one by a professional.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the technical problem to be solved by the invention is to provide a method for estimating and predicting the service life of a bridge based on big data, and the service life of a target bridge can be obtained according to the needs of a user.
The method for estimating and predicting the service life of the bridge based on the big data comprises the following steps: establishing a bridge service life evaluation prediction model based on big data; applying the bridge service life evaluation prediction model to a big data platform facing a target user; inputting characteristic parameters of a target bridge at the big data platform by a target user; the bridge life evaluation prediction model compares the characteristic parameters of the target bridge with the characteristic parameters of the known bridges in the database, and finds out N groups of the closest characteristic parameters and the bridge life; and calculating the service life M of the target bridge according to the weight of the service life of the bridge influenced by the characteristic parameters, wherein M is the bridge service life corresponding to the characteristic parameter 1 and the bridge service life corresponding to the characteristic parameter 2 and the weight N is the weight 2+. the bridge service life corresponding to the characteristic parameter N.
The method for estimating and predicting the service life of the bridge based on the big data provided by the embodiment of the invention at least has the following beneficial effects: the bridge service life detection in the traditional industry and the big data processing of the high and new technology are fused with each other, and a bridge service life assessment and prediction model based on the big data is established, so that the real service life of the bridge can be predicted accurately, the cost of manpower and material resources is greatly reduced, and potential safety hazards are reduced.
According to some embodiments of the invention, the characteristic parameters include one or more of material, load class, construction condition, use condition, degree of appearance damage, geographical location of the location.
According to some embodiments of the invention, the big data on which the bridge life assessment prediction model is based is derived from broken bridge information.
According to some embodiments of the invention, the bridge life assessment prediction model uses an artificial neural network to train big data.
According to some embodiments of the invention, the method further comprises recommending a bridge detection method based on the life M of the target bridge.
According to some embodiments of the invention, further comprising recommending a bridge strengthening method based on the life M of the target bridge.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart of a method for predicting bridge life based on big data evaluation according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.
In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
Because the former bridge construction standard is lower than the current standard and the width of the bridge deck is narrow, although the bridge is reformed for a plurality of times, the bridge is generally aged and seriously damaged because of frequent use. Bridges built in the past have potential hazards to the bridges due to unreasonable design, untight building supervision, shortage of construction materials, insufficient structural systems and untimely maintenance. The traditional bridge is limited by the technical level at the time, the guy cable and the suspender are seriously aged and rusted, the steel bundle and the anchor head are seriously rusted, and the parts on the bridge are easily damaged due to the lack of consideration on the natural environment. Therefore, it is important to establish a scientific evaluation system.
As shown in fig. 1, the method for predicting the life of a bridge based on big data evaluation according to the present invention includes: establishing a bridge service life evaluation prediction model based on big data; applying the bridge service life evaluation prediction model to a big data platform facing a target user; inputting characteristic parameters of a target bridge at the big data platform by a target user; the bridge life evaluation prediction model compares the characteristic parameters of the target bridge with the characteristic parameters of the known bridges in the database, and finds out N groups of the closest characteristic parameters and the bridge life; and calculating the service life M of the target bridge according to the weight of the service life of the bridge influenced by the characteristic parameters, wherein M is the bridge service life corresponding to the characteristic parameter 1 and the bridge service life corresponding to the characteristic parameter 2 and the weight N is the weight 2+. the bridge service life corresponding to the characteristic parameter N.
According to the technical scheme, bridge service life detection in the traditional industry and big data processing of high and new technologies are fused with each other, a bridge service life assessment prediction model based on big data is established, the real service life of a bridge can be accurately predicted, blind overhaul can be avoided, the bridge with high problem probability can be overhauled purposefully, a reasonable inspection period is provided for maintenance work of the bridge, manpower and material resources are saved, and potential safety hazards are reduced.
In some embodiments of the invention, the characteristic parameters include one or more of material, load grade, construction condition, use condition, degree of appearance damage, and geographical location. Of course, other parameters of the whole bridge life process are also possible.
In some embodiments of the invention, the big data on which the bridge life assessment and prediction model is based is derived from the destroyed bridge information, and the more destroyed bridge information is added to the model, the more accurate the assessment and prediction is.
In some embodiments of the invention, the bridge life evaluation and prediction model adopts artificial neural network training big data, and the fault tolerance, robustness and self-organization of the method can be improved by continuously updating the big data source and combining the artificial neural network training.
In some embodiments of the present invention, the method further includes recommending a bridge detection method based on the life time M of the target bridge, where the recommended bridge detection method is derived from broken bridge information, such as an optimal detection method suitable for a frequently damaged location.
In some embodiments of the present invention, the method further comprises recommending a bridge strengthening method based on the life M of the target bridge. The application of big data in bridge durability is actually to predict whether a bridge in normal use needs to be repaired and reinforced through a bridge which is damaged now.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any technical means that can achieve the object of the present invention by basically the same means is within the scope of the present invention.
Claims (6)
1. A method for estimating and predicting the service life of a bridge based on big data is characterized by comprising the following steps: comprises the following steps
Establishing a bridge service life evaluation prediction model based on big data;
applying the bridge service life evaluation prediction model to a big data platform facing a target user;
inputting characteristic parameters of a target bridge at the big data platform by a target user;
the bridge life evaluation prediction model compares the characteristic parameters of the target bridge with the characteristic parameters of the known bridges in the database, and finds out N groups of the closest characteristic parameters and the bridge life;
and calculating the service life M of the target bridge according to the weight of the service life of the bridge influenced by the characteristic parameters, wherein M is the bridge service life corresponding to the characteristic parameter 1 and the bridge service life corresponding to the characteristic parameter 2 and the weight N is the weight 2+. the bridge service life corresponding to the characteristic parameter N.
2. The method for predicting the service life of the bridge based on the big data evaluation as claimed in claim 1, wherein the characteristic parameters comprise one or more of materials, load grade, construction condition, use condition, appearance damage degree and geographical position.
3. The method for predicting the service life of the bridge based on the big data evaluation as claimed in claim 1, wherein the big data based on which the bridge service life evaluation prediction model is based is derived from the destroyed bridge information.
4. The method for predicting the service life of the bridge based on the big data evaluation as claimed in claim 3, wherein the bridge service life evaluation prediction model adopts an artificial neural network to train the big data.
5. The method for predicting the service life of the bridge based on big data evaluation as claimed in claim 1, further comprising recommending a bridge detection method based on the service life M of the target bridge.
6. The method for predicting the service life of the bridge based on big data evaluation as claimed in claim 1, further comprising recommending a bridge strengthening method based on the service life M of the target bridge.
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