The research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

This study focuses on establishing a novel heuristic algorithm for life-cycle performance evaluation. Special attention is given to decision-making algorithms for concrete-filled steel tubular (CFST) arch bridge maintenance. The main procedure is developed, including the ultimate loading-capacity modeling of CFST members, multi-parameter selection, ultimate thresholds presetting based on the finite element method, data processing, crucial parameters determination among sub-parameters, multi-parameter regression, ultimate state prediction, and system maintenance decision-making suggestions based on the multi-parameter performance evaluation. A degenerated ultimate loading-capacity model of CFST members is adopted in the finite element analysis and multi-parameter performance assessment. The multi-source heterogeneous data processing and temperature-effect elimination are performed for the data processing. The key sub-parameters were determined by the Principal Component Analysis method and the Entropy-weight method. The polynomial mathematical model is used in the multi-parameter regression, and the ±95% confidence bounds were verified. The system maintenance decision-making model combines the relative monitoring state, the relative ultimate state by the numerical analysis, and the relative residual life of degenerated members. The optimal system maintenance decision-making suggestions for the bridge maintenance system can be identified, including the most unfavorable maintenance time and parameter index. A case study on a CFST truss-arch bridge is conducted to the proposed algorithms. The obtained results demonstrated that the crack width deserves special attention in concrete bridge maintenance. Additionally, these technologies have enormous potential for the life-cycle performance assessment of the structural health monitoring system for existing concrete bridge structures.

Bridges, as vital infrastructure, require ongoing monitoring to maintain safety and functionality. This study introduces an innovative algorithm that refines bridge component performance assessment through the integration of modified K-means clustering, silhouette coefficient optimization, and cloud model theory. The purpose is to provide a reliable method for monitoring the safety and serviceability of critical infrastructure, particularly double-layer truss arch bridges. The algorithm processes large datasets to identify patterns and manage uncertainties in structural health monitoring (SHM). It includes field monitoring techniques and a model-driven approach for establishing assessment thresholds. The main findings, validated by case studies, show the algorithm’s effectiveness in enhancing clustering quality and accurately evaluating bridge performance using multiple indicators, such as statistical significance, cluster centroids, average silhouette coefficient, Davies–Bouldin index, average deviation, and Sign-Rank test p-values. The conclusions highlight the algorithm’s utility in assessing structural integrity and aiding data-driven maintenance decisions, offering scientific support for bridge preservation efforts.

Bridge Health Monitoring (BHM) systems generate nonstationary time-series data that pose challenges for accurate structural state prediction. This study proposes a novel neural network-based method for predicting bridge states, the Temporal Multi-Scale Convolutional Neural Network (T-MSCNN) to enhance the prediction of dynamic strain parameters, which directly reflect structural stress states. The T-MSCNN integrates multi-scale convolutional layers for local feature extraction and gated recurrent units (GRUs), by using the Convolutional Neural Network (CNN) model and the Gated Recurrent Unit (GRU) model to address the intricacies of the nonstationary BHM data. Validated with real strain data from the Qijiang River Bridge in China, the model demonstrated superior performance over traditional models (HA, ARIMA, SVR) and standalone deep learning models (CNN, GRU), achieving reductions in prediction error by Root Mean Square Error (RMSE) method—up to 77.7%, compared to the ARIMA model and consistently improving even over the strong GRU baseline. The perturbation analysis confirms its robustness under noise interference. The T-MSCNN provides a reliable data-driven framework for structural health diagnostics, with potential applicability to other fields involving nonlinear spatiotemporal data analysis.

The research proposes a hybrid algorithm model that combines model-driven and data-driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi-objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi-source heterogeneous data, selection of key sub-parameters using Principal Component Analysis (PCA), enhanced K-means clustering analysis, determination of structural component target thresholds, time-dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double-layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub-parameters that have significant impacts on the performance of structural components. Enhanced K-means clustering analysis is carried out to effectively group and classify the identified key sub-parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi-parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan-Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double-layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and the bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.