5 results about "Structural condition" patented technology

The invention provides a fabricated steel structure column base anti-seismic reinforcing device, and relates to the technical field of steel structures, the fabricated steel structure column base anti-seismic reinforcing device comprises a steel column, an upper side column base bottom plate is welded to the bottom end face of the steel column, and a lower side column base bottom plate consistent with the upper side column base bottom plate in structural size is arranged below the upper side column base bottom plate; a double-elastic buffering system is formed through the rubber buffering compensation sleeve and the rubber anti-vibration buffering cushion, the rubber buffering compensation sleeve is in sliding fit with the limiting insertion column, horizontal and vertical vibration energy is absorbed through elastic deformation of the rubber buffering compensation sleeve, and a vibration transmission path is weakened; the rubber anti-seismic cushion further buffers vibration impact force through compression rebound deformation, meanwhile, the vibration amplitude is attenuated by means of the high damping characteristic of rubber materials, the structure resonance risk is effectively reduced, impact caused by medium-high-strength earthquakes can be better resisted, and the service life of the anti-seismic cushion is prolonged. The problems that an existing fabricated steel structure column foot is insufficient in anti-seismic buffering performance, weak in shear resistance and limiting capacity and inconvenient in structural state monitoring are solved.

This invention relates to the field of bridge structural health monitoring and reinforcement optimization technology, providing a method and system for analyzing the structural condition of old concrete T-beam bridges based on multi-data fusion. The method includes: predicting the comprehensive impact value of eccentric loading on the side beams of the target old concrete T-beam bridge within a preset operating cycle; when the comprehensive impact value of the eccentric loading exceeds a judgment interval, generating a correction factor based on the degree of exceedance relative to the judgment interval; and correcting the initial repair and reinforcement parameters according to the correction factor to obtain optimized repair and reinforcement parameters. This invention identifies anomalous intervals of coordinated response that are difficult to detect using traditional methods, and on this basis, introduces a correction factor based on the degree of exceedance to continuously and quantitatively adjust the initial repair and reinforcement parameters, improving the matching between the repair plan and actual operating conditions. This invention can improve the targeting and structural safety of side beam repair, reduce the risk of misjudgment, and has good engineering application value.

The invention relates to a method for evaluating the structural rigidity of a large-span track bridge under the action of a no-load first train, and belongs to the technical field of track bridge structural state evaluation. The method includes: performing numerical simulation analysis according to a large-span track bridge structure; determining the mounting positions of the deformation measuring points of the most unfavorable section Kmax of the transponder Y and the main beam of the rail train; respectively acquiring the time when the train head passes through the position Y0 of the transponder, the time when the train center passes through the position Ymax of the transponder and the time when the train tail passes through the position Yz of the transponder; acquiring a deformation monitoring value of the most unfavorable cross section of the main beam under the action of the no-load first train in the same environmental factor condition; the correction coefficient of the most unfavorable cross section deformation of the main beam is deduced; and analyzing, studying and judging to provide treatment opinions. The invention provides a method for evaluating the structural rigidity of a large-span rail bridge under the action of a no-load first train, which fully considers the mapping relation between the deformation monitoring value and the theoretical calculation value of the large-span rail bridge under the action of the no-load first train, and provides technical support and scientific basis for the safe operation of the large-span rail bridge.

This application relates to the field of structural health monitoring technology, and discloses a method, device, equipment, and storage medium for structural condition monitoring, including: acquiring vibration signals and image sequences of a target structure; extracting features from the vibration signals to obtain a modal parameter set, and extracting features from the image sequences to obtain an apparent deformation parameter set; performing temporal alignment and adaptive weighted fusion of the features in the modal parameter set and the apparent deformation parameter set to obtain fused features; inputting the fused features into a random forest convolutional neural network hybrid model, outputting the anomaly probability of the target structure, and determining the health status of the target structure based on the anomaly probability. By simultaneously acquiring vibration and visual data, and extracting corresponding features for fusion, a mapping relationship reflecting internal dynamic characteristics and external geometric deformation is established, overcoming the shortcomings of traditional single monitoring methods with incomplete dimensions, and realizing integrated, high-precision monitoring and early warning of internal structural damage and apparent deformation.

This application discloses a method, estimation method, apparatus, device, and medium for generating a digital twin model for joint estimation of structural load and response. The method includes determining the output estimated load based on the trained structural response using an encoder; extracting graph structural feature vectors based on the trained structural response using a graph-based digital twin module; determining the predicted structural response based on the graph structural feature vectors and the estimated load using a decoding module; constructing a loss function value based on the predicted structural response; and training the digital twin model based on the loss function value. This application estimates the structural load using an encoder without requiring any prior load information, and inputs the estimated load into a decoder to construct a high-precision structural response digital twin model. This achieves unsupervised load estimation, improving the accuracy, stability, and reliability of structural condition assessment in civil engineering projects under complex and variable external environments.