353 results about "Bridge maintenance" patented technology

The invention discloses a hollow slab girder bridge maintenance decision-oriented hierarchical knowledge graph construction method, which comprises the following steps of: constructing a hollow slab girder bridge disease classification and maintenance decision general knowledge graph, combing disease types and evaluation indexes of a hollow slab girder bridge through cross-standard feature alignment and rule fusion, and defining each disease and the index thereof in a variable manner so as to construct a hollow slab girder bridge disease classification and maintenance decision-oriented hierarchical knowledge graph. The weight of each disease index is determined based on a fuzzy-Bayesian hybrid reasoning algorithm, and a complete general knowledge graph structure is formed; extracting disease information based on a detection report of a single bridge, endowing each disease with a maintenance suggestion by using a general knowledge graph, and forming a maintenance decision knowledge graph special for the bridge; and dynamically updating the special knowledge graph, and adjusting disease information and maintenance suggestions according to a subsequent detection report of the bridge to ensure timeliness and accuracy of the graph. According to the method, the problems that knowledge is dispersed and systematization is difficult in traditional hollow slab girder bridge maintenance decision making are solved, and scientificity and efficiency of bridge maintenance management are improved.

The invention relates to the technical field of bridge engineering construction, in particular to a bridge deck concrete flatness detection and correction method, which comprises the following steps: synchronously acquiring bridge deck point cloud, elevation and crack data through a three-dimensional laser scanner, a laser profiler and an ultrasonic sensor, and fusing to generate a three-dimensional digital model; defects such as local protrusions and wave deformation are recognized based on an intelligent analysis algorithm, causes are analyzed, and correction process parameters such as milling, cladding, grouting or ultrathin layer pouring are generated in a targeted mode; operation is executed through equipment such as a finish milling machine, closed-loop control is realized by combining real-time monitoring of a laser profiler, and finally, a traceable digital file is formed through laser detection, inspection and acceptance of an unmanned aerial vehicle and data archiving. By means of the method, the pass percent of the flatness of the bridge floor can be increased to 94% or above, the maximum height difference is controlled within 3 mm, the maintenance period of the bridge is prolonged, and the maintenance cost is reduced.

The invention relates to a bridge technical condition prediction method based on category clustering and trend modeling fusion, belongs to the technical field of bridge state prediction and maintenance management, and solves the problems of limited generalization ability and low prediction accuracy of an existing prediction model. The method comprises the following steps: firstly, collecting regular detection data of a plurality of bridges and preprocessing; after feature coding and dimension reduction are carried out, a K-means clustering algorithm is adopted to group bridges, and an intra-class degradation trend model is established by using a Huber regression method; an XGBoost regression method is adopted to establish an intra-class technical condition prediction model; according to the intra-class technical condition prediction model and the intra-class degradation trend model, bridge technical condition prediction results of the target bridge in multiple prediction years in the future are generated through a dynamic weighted fusion formula; and finally performing model performance evaluation. According to the method, the accuracy of bridge technical condition prediction can be effectively improved, reliable long-term degradation trend prediction is provided, and the maintenance decision of the bridge is optimized.

The invention provides a bridge group maintenance priority dynamic decision-making method and device based on reinforcement learning, and relates to the technical field of bridge intelligent maintenance. The method comprises the following steps: constructing a topological structure of a bridge network and a road; defining a state space, a maintenance action space and a state transition matrix of the bridge; defining a reliability index corresponding to the state of the bridge, and designing a comprehensive reward function based on maintenance cost, asset risk and traffic network capacity loss risk based on the topological structure; constructing a bridge maintenance decision problem; the method comprises the following steps of: describing a bridge maintenance decision problem as a Markov decision process, establishing a pointer network strategy model by adopting a pointer network, and training the pointer network strategy model by adopting an Actor-Critic algorithm to obtain a maintenance decision model based on reinforcement learning; and training the maintenance decision model based on reinforcement learning until convergence, and outputting a bridge maintenance action sequence under limited constraints. By adopting the method, the limitation problem of traditional single bridge assessment can be solved.

The invention provides a municipal bridge damage defect detection method and a municipal bridge damage defect detection system, which are used for solving the problem that the quality and the accuracy of acquired data are difficult to guarantee due to the fact that vibration signals are easily interfered by environmental noise, such as vehicle load, wind load and the like. A bridge key part vibration signal data set is obtained, and a final damage area range is determined based on the vibration signal data set; whether the bridge is damaged or not is judged based on recognition of the vibration signals, so that the signal-to-noise ratio of monitoring data and the damage positioning precision are improved, a more accurate and reliable decision basis is provided for a bridge management department, and finally maintenance and reinforcement work of the bridge is effectively guided.

The invention relates to the field of bridge engineering, in particular to a bridge structure toughness dynamic evaluation method and system based on digital twinning, and the system firstly collects the environment, geometry and performance information of a bridge, and then establishes an accurate bridge digital twinning model; a bridge structure toughness prediction model is systematically constructed by using a space-time diagram convolutional network, and the performance degradation process of a bridge in extreme weather can be predicted; meanwhile, toughness evaluation indexes are formulated by the system, and accurate performance evaluation of real-time monitoring data is realized; through multi-source data fusion, the system improves the comprehensiveness and accuracy of data acquisition; the prediction model based on the space-time diagram convolutional network significantly improves the accuracy of performance degradation prediction; a scientific and efficient tool is provided for bridge maintenance and management, and safe operation of the bridge in extreme weather is effectively guaranteed.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a bridge maintenance system based on data analysis, which comprises a multi-source data acquisition module, a fatigue degradation monitoring module, a wave velocity anomaly positioning module, a risk collaborative judgment module and a maintenance task planning module. According to the method, structural fatigue characteristics are subjected to cross validation by integrating two-dimensional data of a strain sequence and a stress wave propagation parameter, and a dynamic window range deviation degree is adopted to replace a static threshold value to establish a datum reference linked with a bridge bearing period; a same-path propagation difference value is extracted by using a high-frequency and low-frequency stress wave frequency band energy separation technology to detect continuous overrun, and a degradation trend weight and a priority list of damage space distribution are generated in combination with multi-index coexistence verification and a hierarchical sorting strategy; resource allocation is optimized by integrating a multi-level response mechanism of emergency repair coordinate positioning, directional detection and regular inspection, accurate crack positioning is achieved, the early degradation trend capturing capacity is enhanced, the misjudgment risk is reduced, and an operation and maintenance response chain is shortened.

The invention relates to a bridge technical condition evaluation method and system. The method comprises the steps that bridge basic information input by a user is acquired; generating bridge structure information according to the bridge basic information; determining a disease type and a disease index of each part according to a bridge disease library; according to the disease type and the disease index of each part, calculating the score of each part deducted according to the disease; calculating the score of each part type in each structure according to the score of each part deducted according to the disease; redistributing the weight of each part type in each structure, and obtaining the technical condition score of each structure based on the score of each part type in each structure; performing weighted calculation on the technical condition score of each structure to obtain an overall technical condition score of the bridge; and determining a technical condition grade according to the overall technical condition score of the bridge. According to the invention, automatic evaluation of the bridge state is realized, and the efficiency and accuracy of bridge maintenance and management are greatly improved.

The invention relates to a large model-assisted bridge knowledge graph generative construction method, which comprises the following steps of S1, extracting bridge related entities in a document based on a pre-trained large language model, and obtaining a structure ontology model and structure ontology attributes; the method comprises the following steps: performing hierarchical design on a bridge management and maintenance knowledge graph, performing knowledge graph construction through more than two pre-trained large language models, dividing the knowledge graph into a generator and a discriminator, and performing entity and attribute extraction by using the large language models of the generator; whether entities and attributes are reasonable or not is judged through a large language model of a discriminator; s2, a disambiguation method based on a graph neural classification network; and S3, a knowledge graph reasoning method based on a component relationship. Compared with the prior art, the method is innovatively combined with a large model, so that the entity extraction efficiency and accuracy are improved; the availability of the knowledge graph and the reasoning accuracy are improved; the knowledge graph reasoning method based on the component relation is provided according to the actual requirements of bridge maintenance decision making.

The invention relates to the technical field of engineering structure health monitoring, and discloses a method for evaluating the fatigue life of a main beam based on sample continuous increment, which comprises the following steps: acquiring multi-modal monitoring data including physical parameters of strain, displacement and acceleration and external factor data of environmental temperature and humidity and traffic flow information; performing multi-source data fusion by using an adaptive feature extraction network; constructing a dual-knowledge consolidation incremental learning framework, and keeping the memory of historical knowledge when newly added data is processed; performing uncertainty quantification on a prediction result based on Bayesian deep learning, and establishing a risk early warning mechanism; according to the method, the problem of disastrous forgetting in the model updating process is effectively solved, and a relatively high historical knowledge retention rate is kept; a scientific basis is provided for bridge maintenance and maintenance decision making, and maintenance resource allocation is optimized; the method has excellent adaptability and generalization ability, and realizes continuous monitoring and evaluation in the whole life cycle.

The invention belongs to the technical field of bridge maintenance, and discloses a machine vision and digital twinning combined remote repair welding repair method and system for a cracked steel bridge structure. A transmission steel bridge maintenance method mainly depends on manual detection and field repair, and has many limitations in practical application and is difficult to meet bridge maintenance requirements. The method comprises the following steps: acquiring steel bridge data through a detection robot to identify a steel bridge crack, generating a three-dimensional model of the steel bridge crack based on the steel bridge crack data, performing finite element stress analysis, generating a crack welding strategy, and issuing the crack welding strategy to a welding robot to execute a welding action; further dynamically mapping the welding information to the three-dimensional model of the crack, fusing the three-dimensional dynamic model of crack repair into a VR operation interface, and monitoring the welding process of the welding robot through the VR operation interface; and finally, the welding robot detects the repair integrity of the steel bridge crack, and the repair integrity information is updated to the three-dimensional dynamic model of steel bridge crack repair.

The invention discloses a road and bridge expansion joint defect detection system based on image recognition. Real-time image data of a road and a bridge are acquired based on a cruise image acquisition sensor; the method comprises the following steps: performing coordinate system matching on real-time image data and a standard image, eliminating a visual angle difference by adopting an SIFT (Scale Invariant Feature Transform) feature point extraction algorithm and homography matrix transformation, comparing and aligning the real-time image data and the standard image through a double-encoder-decoder network, and judging whether a defect exists in a suspicious defect area or not by using a MobileNetV3 classification model. If the defects exist, a ResNet-50 model is adopted to classify the types and severity of the defects, and cross validation is carried out on defect detection results and vibration characteristics of the bridge and the road. The method can have high classification precision, provides an accurate decision basis for maintenance and management of roads and bridges, guarantees the detection efficiency, and improves the detection accuracy.

The invention relates to a bridge degradation identification and maintenance decision support method and system based on a knowledge graph, belongs to the technical field of traffic infrastructure intelligent operation and maintenance and engineering information processing, and solves the defects of weak data fusion capability, opaque causal modeling and non-traceable decision support in the prior art. The method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data to generate a standardized data set; semantic alignment is carried out, and candidate entities and relations are extracted; constructing a knowledge graph body and loading the knowledge graph body into a knowledge graph; a self-adaptive quantile threshold value is adopted to binarize the factor intensity and the disease severity sequence, and a smooth point mutual information and a Spearman correlation coefficient are fused to learn a causal weight; complementing the influence relation of factors on the bridge through graph reasoning and calculating the weight; and finally, calculating a bridge degradation risk score, and outputting a high-confidence influence path based on a shortest path algorithm. According to the method, multi-source data fusion and degradation causal chain explicit modeling are realized, and a scientific basis is provided for a bridge maintenance decision.

The invention relates to the technical field of bridge engineering maintenance decision, and discloses a bridge maintenance decision method based on big data. The method comprises the steps of collecting bridge monitoring data through a sensor network, performing hierarchical processing on an original data stream, identifying and packaging abnormal fluctuation, trend change and a periodic mode, and forming a structured health event sequence. And dynamically constructing and updating the digital sign profile of the bridge in combination with the design parameters and the material degradation model. And when a decision is triggered, simulating a performance evolution path under multi-factor coupling based on the profile, generating a future maintenance scene set, and matching and assembling a preliminary plan from historical cases and the knowledge graph. And outputting a final maintenance instruction list sorted according to priorities by performing resource and time coupling analysis and multi-target tradeoff on all plans. According to the method, deep analysis and prospective simulation of the bridge state are realized, and the intelligence, accuracy and performability of maintenance decision making are improved.

The embodiment of the invention provides an intelligent maintenance device for hollow slab girder inner cavity operation, and relates to the technical field of bridge maintenance. The supporting leg controller is started, the crawler-type wheel sets are tightly jacked in a bottom plate of an inner cavity of the hollow slab girder through the foldable supporting legs, the driving wheels are driven by the wheel set control system, the driven wheels are driven by the driving wheels to rotate, the crawler belts are driven by the driving wheels and the driven wheels to move, and the movement of the crawler belts enables the maintenance device to move in the inner cavity of the hollow slab girder. Efficient and convenient polishing treatment operation can be conveniently carried out on the inner cavity of the hollow slab beam; a brush head connecting rod of the polishing assembly is adjusted through a telescopic connecting piece to adapt to the space size of the inner cavity; a grinding assembly disc is started, eight telescopic grinding brushes are distributed on the grinding assembly disc, and comprehensive and careful grinding operation on the inner cavity of the hollow slab girder can be achieved through rotation of the grinding assembly disc; construction waste such as powder generated by grinding enters a waste collecting box through a pipeline in the middle of the grinding brush via the interior of a disc of the grinding assembly; a series of high-definition cameras are arranged in the circumferential direction of the detection and evaluation disc, effect pictures of the inner cavity after polishing are collected and input into an evaluation system to automatically evaluate the roughness after polishing, and therefore the requirement for intra-cavity grouting maintenance is met. The device can greatly improve the quality and efficiency of polishing operation, so that reliable cohesiveness of an inner cavity of the hollow slab beam and a grouting maintenance material is guaranteed, and the shear bearing capacity of the hollow slab beam is improved.

A sensor-based digital twin system operable to determine bridge load rating, enhance bridge safety, extend infrastructure lifespan, and reduce maintenance costs of a bridge. The sensor-based digital twin system uses real-time data to update virtual models of physical assets that enables a more dynamic and precise understanding of bridge conditions, which allows for real-time monitoring of a load assessment of a bridge and can identify the need for bridge maintenance.

The utility model relates to a movable construction platform of a cable-stayed bridge tower column, and belongs to the technical field of bridge maintenance. The two C-shaped platform modules are connected in a closed mode to form an all-dimensional construction platform structure special for surrounding the side face of the bridge tower column of the cable-stayed bridge, and the all-dimensional construction platform structure can be lifted to any height position to conduct construction on the bridge tower column under the cooperation of a suspension system, a lifting machine and a steel wire rope. Meanwhile, aiming at the side face of the bridge tower column with the variable section size, a linear telescopic platform on the inner side is arranged on the basis of the C-shaped platform module for further distance reduction, small-range space adjustment can be conducted when the C-shaped platform module is close to the side face of the bridge tower column, and an auxiliary basket set hung on one side is arranged. And the construction personnel can further conveniently, quickly and directly adjust the operation space at a small distance in the operation process.

The utility model relates to the technical field of bridge maintenance, in particular to a bridge maintenance overhauling device which comprises a vehicle body, a sliding frame, a lifting driving assembly and a material transferring assembly. The vehicle body is rotationally connected with the support and provided with a driving assembly for driving the support to rotate. The sliding frames are symmetrically arranged on the support and are in sliding connection with the support, the sliding frames on the two sides are connected with the same mounting plate, and a winch A is arranged on the mounting plate and is in driving connection with a hanging basket A. The lifting driving assembly is connected with the sliding frame and the limiting assembly, the limiting assembly is arranged between the hanging basket and the driving assembly, and the limiting assembly is clamped on the bridge guardrail in the working state and moves along with the vehicle body. The material transferring assembly is in sliding connection with the support, and the material transferring assembly transfers materials or tools to the hanging basket A in the working state. The construction hanging basket can be effectively supported and protected, and under the condition that the moving direction of the vehicle body deviates, the hanging basket cannot collide with a bridge guardrail all the time.

The invention discloses a reinforcing system and method for structural damage of an arch bridge, and belongs to the technical field of bridge engineering. A prestressed carbon fiber grid reinforcing layer (material reinforcement), SMA anchorage device intelligent regulation and control (parameter self-adaption) and magneto-rheological damper active control (dynamic response optimization) realize deep coupling through a distributed sensing network and edge calculation to form system-level collaboration of strength improvement, rigidity adjustment and dynamic performance optimization; the subsystems are driven to work cooperatively through sensing data, and a new'prevention-reinforcement-regulation 'integrated bridge maintenance mode is constructed.

The invention provides a bridge deformation detection method based on three-dimensional laser scanning and a multi-scale algorithm, and the method specifically comprises the steps: carrying out the detection of the overall and local deformation of a bridge through employing the fusion of a three-dimensional laser scanning technology and the multi-scale algorithm, the method comprises the following steps: firstly, by introducing a geometric constraint registration algorithm, a cascade noise reduction model and a multi-stage slice center point offset analysis technology, realizing synchronous accurate quantification of local settlement of a bridge floor and overall inclination of a bridge pier; and in combination with load distribution verification, the spatial correlation between the deformation rule and the traffic load is revealed, and a scientific basis is provided for bridge safety assessment and reinforcement decision making. The bridge safety monitoring precision is remarkably improved, intelligent system development is supported, and guidance is provided for bridge maintenance.

The invention relates to the technical field of bridge health monitoring, in particular to a bridge support corrosion current field imaging monitoring method and system, and the method comprises the steps: presetting a matrix type electrochemical sensor on a support steel plate, and collecting the inductance data of a collector at a sampling frequency of 0.1-0.5 Hz; ohmic resistance drift caused by temperature is eliminated through adaptive quantum Kalman filtering, and a resistance-current density distribution diagram is constructed; constructing a current density distribution diagram of the cross section of the support by adopting a back propagation electrical impedance tomography technology; when the corrosion rate exceeds 0.15 mm / year, an early warning system is triggered, the bearing capacity attenuation rate of the support is calculated, and the support is guided to be replaced; a topological separation technology of a multi-dimensional state space and quantum heuristic adaptive parameter optimization effectively eliminate temperature interference; through current field topology reconstruction and multi-source data fusion, the imaging precision is improved; the quantitative relation between the corrosion rate and bearing capacity attenuation of the support is established, and a scientific basis is provided for bridge maintenance.

The invention relates to the technical field of bridge maintenance, in particular to a road and bridge spraying maintenance system and a control method thereof, and the system comprises a water supply execution module, a regional spraying module, an intelligent control module, a remote response module, a dynamic adaptation module, a parameter setting module and a regional adaptation module. The water supply execution module provides a pressure water source through an integrated water tank and a booster pump. A water supply header pipe is arranged along the inner side of a bridge deck guardrail. The regional spraying module is provided with spraying assemblies and box chamber inner cavity adjustable sprayers in a partitioned mode. The intelligent control module dynamically adjusts spraying parameters and opening and closing of branches; the dynamic adaptation module realizes displacement synchronization of the pipeline and the hanging basket through a quick disassembly joint and a main pipe extension section; the parameter setting module switches the spraying mode according to the concrete age and the surface temperature; the area adaptation module controls pipeline separation and connection and nozzle angle correction. The problems of insufficient corner coverage and poor timeliness are solved, and the maintenance uniformity and the structural durability are improved.

The utility model relates to the technical field of expansion joint distance measurement, in particular to a municipal bridge expansion joint width real-time distance measurement device, which comprises two grab handles, a measuring mechanism is fixed at the rear end of each grab handle, a supporting mechanism is fixed at the rear end of each measuring mechanism, and an inner supporting rod mechanism is mounted between the two supporting mechanisms. An outer supporting rod mechanism is installed between the two measuring mechanisms. The measuring mechanism comprises a mounting seat and a connecting plate, the connecting plate is fixed at the rear end of the mounting seat, positioning seats are fixed on two sides of the front end of the connecting plate, a laser range finder is mounted on the inner side of the mounting seat, a detection bolt is in threaded connection with the middle of the outer side of each positioning seat, and a lead rod is fixed at the front end of each detection bolt. And the device can change along with the width change of the expansion joint, so that the actual width of the expansion joint can be truly reflected by the measurement data, and a reliable basis is provided for municipal bridge maintenance.

The invention discloses a bridge bottom construction maintenance trolley, which relates to the technical field of bridge maintenance, and comprises a suspension trolley, a truss hanging basket channel, a working rack, a safety ladder, a suspension cable and an electromagnetic swing stabilizer, the two sets of suspension cables are connected to the two ends of the truss hanging basket channel and the suspension vehicles on the two sides of the bridge floor correspondingly, and the cable adjusting units control the suspension cables to be wound and unwound so as to adjust the distance between the truss hanging basket channel and the bridge bottom. The working rack is arranged on the truss hanging basket channel and is used for operators to approach to the bridge bottom for high-altitude operation; one end of the safety ladder is mounted below a suspension arm of the suspension vehicle, and the other end of the safety ladder is connected with a truss hanging basket channel and contains the suspension cable; the electromagnetic swing stabilizer is arranged on a suspension arm of the suspension vehicle, is connected with the suspension cable through a plurality of electromagnetic adjusting cylinders, and is used for counteracting shaking generated during operation and stabilizing the postures of the suspension cable and a truss hanging basket channel; the problems of work amount, efficiency, safety, operation range and flexibility of existing bridge bottom maintenance are solved, and efficient, safe and flexible bridge bottom maintenance operation is achieved.

The invention discloses a concrete bridge preventive maintenance decision-making method based on degradation prediction, and relates to the technical field of bridge degradation prediction maintenance, and the method comprises the steps: collecting the degradation characteristic data of a concrete bridge through a bridge detection device; according to the method, various types of degradation characteristic data are collected through the bridge detection equipment, the standardized bridge state characteristic vector set is generated, the data-mechanism fusion inversion model is adopted, the actually collected data and the inherent mechanism of bridge degradation are combined, the estimation result is made to better conform to the actual situation, and the estimation accuracy is improved. Dynamic data assimilation is carried out according to a state space model, a probabilistic estimation set of a degradation driving state is input into a predetermined bridge degradation mechanism model for long-term deterministic simulation prediction to generate a plurality of future performance degradation paths, and all the paths form a degradation track cloud picture reflecting prediction uncertainty; the optimal maintenance strategy set can reduce the expected life cycle cost to the maximum extent on the premise that the bridge maintenance requirement is met.

The invention discloses a bridge maintenance dynamic decision-making method and system based on multiple dimensions and maintenance standards, and belongs to the technical field of bridge intelligent maintenance, and the method comprises the steps: collecting static basic data and dynamic basic data of a to-be-maintained bridge, and evaluating and determining key parts / components, key diseases, four-stage maintenance standards and three-dimensional indexes; a maintenance scheme library formed by different maintenance schemes is determined, and a to-be-maintained bridge and a maintenance project demand analysis library of the maintenance project amount are screened and determined; analyzing and generating a cost benefit model; and according to the cost-benefit model, calculating the maintenance cost required by the to-be-maintained bridge corresponding to different maintenance standards in the maintenance project demand analysis library and the maintenance benefit generated by maintenance, determining a maintenance project plan library and making a maintenance plan. The bridge maintenance dynamic decision-making method and system disclosed by the invention are helpful to grasp the development trend of bridge technical conditions, standardize maintenance decision-making behaviors, improve the scientific decision-making level and effectively carry out preventive maintenance.

The utility model relates to the technical field of bridge pier maintenance, and provides a bridge pier maintenance device which comprises two receiving boxes, protective cloth, fixing rings, a moving mechanism, a spraying mechanism and a locking mechanism, the two receiving boxes are rotatably connected, the protective cloth is fixedly connected to the two receiving boxes, the fixing rings are fixedly connected to the two protective cloth, and the moving mechanism is connected with the spraying mechanism. Moving mechanisms are arranged on the two fixing rings and used for driving the fixing rings and the protective cloth to ascend and descend on the pier, spraying mechanisms are arranged on the two fixing rings and used for spraying the pier, and locking mechanisms are arranged on the two fixing rings and the two receiving boxes and used for locking the two fixing rings and the two receiving boxes. By means of the technical scheme, the problems that in the related technology, after water is manually sprayed to the bridge pier, water of the bridge pier can be rapidly evaporated in air, time and labor are wasted, and meanwhile the bridge maintenance efficiency and effect are reduced are solved.

According to the bridge pier local scour depth prediction method and the prediction system thereof provided by the invention, related parameters influencing the pier local scour depth are determined, the BP neural network is constructed, the corresponding parameters are determined as final input quantities, and the BP neural network is utilized to perform correlation analysis on the influence of each parameter, so that the prediction accuracy of the local scour depth of the bridge pier is improved. Compared with the prior art, independent constant value calculation is not needed, and due to the error back propagation characteristic of the BP neural network, the error amount of final prediction is small and is close to an actual value, so that the local punching depth of the pier can be accurately predicted, and accurate data support is provided for follow-up bridge maintenance.

The invention relates to the technical field of rail transit bridges, in particular to a rail transit bridge maintenance decision-making method based on an analytic hierarchy process and a matrix type double-control method. Comprising the following steps: S1, decomposing and hierarchically dividing components in the rail transit bridge to obtain a clear hierarchical structure; s2, performing pairwise comparison on the components by adopting an analytic hierarchy process to form a judgment matrix, and calculating a consistency index and a feature vector to obtain a relative weight of each hierarchy and the component; s3, evaluating and scoring bridge diseases by using a matrix type double-control method; and S4, based on the comprehensive score of the matrix type double-control method, the maintenance decision of the rail transit bridge can be judged. Compared with the prior art, the analytic hierarchy process and the matrix type double-control method improve the accuracy and practicability of the maintenance decision through iterative optimization, the importance and health conditions of different components in the rail transit bridge maintenance decision can be comprehensively evaluated, and reliable technical support is provided for the maintenance decision and maintenance management of the rail transit bridge.

The utility model discloses a welding device for repairing a bridge inhaul cable, which relates to the technical field of bridge maintenance and comprises an inner sheath, outer sheaths are rotatably mounted on the front side and the rear side of the inner sheath respectively, and the two outer sheaths are rotatably connected with the inner sheath through two rotating assemblies respectively. Each of the inner sheath and the two groups of outer sheaths comprises two shells which are symmetrically distributed up and down, two groups of locking assemblies are symmetrically and fixedly mounted on the left and right sides of the shells of the inner sheath and the two groups of outer sheaths, and the arc-shaped groove of the driving wheel is tightly attached to the curved surface of the inhaul cable, so that the contact area and the friction force are effectively increased; therefore, it is ensured that the device has stable driving adhesive force and crawling capacity on an inclined or vertical cable face, self-locking is formed through a locking assembly and a limiting tooth block pressed by a spring through a rack, secondary fastening is assisted by a bolt, double anti-sliding guarantee is provided for an operation position, displacement generated in the welding process is prevented, and the service life of the device is prolonged. And the precision and continuity of repairing operation are ensured.