This paper proposes the use of Gaussian Bayesian networks (GBNs) for damage detection of steel truss bridges by using the strain monitoring data. Based on the proposed damage detection procedure, a three-layer GBN model is first constructed based on the load factors, structural deflections, and the stress measurements of steel truss bridges. More specifically, the load factors of the structures are defined as the first-layer network nodes, structural deflections are considered as the second-layer network nodes, and the third-layer nodes of the GBN model are built based on the stress data of the truss elements. To achieve the training for the constructed GBN model, the finite element analysis of the bridge structures under the different load factors is performed. Then, the training of the network is performing by using the maximum likelihood estimation approach, and the optimized network parameters are obtained. Based on the trained network model, the measured load factors and the corresponding stress monitoring data of a limited number of truss elements are considered as input, and the stress measurements of all truss elements of bridges can be accurately estimated by searching the optimized topological information among network nodes. For a steel truss bridge, when the truss elements are damaged, the stress states of the damaged elements will be changed. Therefore, a damage index is further constructed for damage detection of steel truss bridges based on the changed stress states of those damaged elements. To verify the feasible and effective use of the proposed damage detection approach, an 80 m steel truss bridge with various damage cases was conducted as numerical simulations, and the investigation results show that the trained GBN can be accurately used for stress prediction of steel truss bridges, and the proposed damage index with the estimated stress data can be further applied for structural damage localization and quantification with a better accuracy. Furthermore, the results also suggest that the proposed damage detection procedure is accurate and reliable for steel truss bridges under vehicle loads.

Due to a series of societal factors, management of small rural water resources projects in China experience management problems. Based on the management mode of small water resources projects in three representative regions of Guangdong Province, the improved TOPSIS model is applied to evaluate the performance of management mode of small water resources projects through the combination with entropy weight method. Compared with the traditional TOPSIS model concerning the evaluation object of this paper, the evaluation value formulas of optimal and worst solutions of TOPSIS method are improved. The evaluation index system takes into account the coverage, hierarchy and systematization of indicators, and maintains a management mode with high environmental adaptability, so that the continuous operation of management mode can guaranteed. The results show that the management mode of water user association is most suitable for the development of small water resources projects in Guangdong Province.

To investigate the factors influencing existing tunnels adjacent to foundation pit excavations, this paper focuses on a case of excavation of MTR station foundation pits near an existing tunnel. Monitoring data are collected and analyzed, and a two-dimensional numerical model of the pit–tunnel system is established using ABAQUS (2022) software. This study examines the effect of excavation factors on the tunnel, including excavation depth, horizontal spacing between pit and tunnel, support pile wall dimensions, support structure insertion ratio, horizontal support stiffness, and geological conditions. The research results indicate that for tunnels in silty clay formations, the depth of pit excavation should be limited to 20 m. When the horizontal spacing between a foundation pit and tunnel in a silty clay formation is within 1.5 times the depth of excavation, the tunnel is more significantly affected by tunnel excavation works in the adjacent pits. Increasing the horizontal support stiffness has the greatest effect on controlling deformation and stress in the tunnel structure. The degree of influence on deformation and stress in the tunnel due to laterally adjacent excavation of a foundation pit varies with geological conditions. Among them, tunnels in muddy-silty clay and silty soils are the most affected, and it is recommended that reinforcement measures be optimized and monitoring be strengthened. The conclusions of the present study can provide a reference point and guidance for the optimal design of similar pit projects.

The axial force of steel bracing is one of the essential indexes to measure the stability of the bracing system of a foundation pit. The steel bracing system of a foundation pit in Ningbo City, China was taken as the research object to guarantee the stability of the steel bracing system of the foundation pit. Besides, the change of axial force between the two steel bracing structures was analyzed to predict the axial force data of the steel bracing and perform the safety forewarning of the steel bracing system. Firstly, GM (1,1) and Verhulst models in the gray model were selected for prediction based on the characteristics of poor information and the small sample size of original monitoring data of the steel bracing. Secondly, the precision of the GM (1,1) model and Verhulst model was compared to determine a more accurate prediction method. Finally, the safety forewarning model of the confidence interval estimation method was established based on the data obtained from the prediction model and the deformation characteristics and indexes of the steel bracing. With the significance levels α = 5% and α = 2% as the demarcating points, the forewarning grades of the steel bracing system of the deep foundation pit were divided, and then the operating state of the current steel bracing system was determined. The results demonstrated that the Verhulst model had better prediction precision compared with the ordinary GM (1, 1) model. Besides, the steel bracing system was in the safe operation range, and the judgment results of the model were consistent with the actual situation of the foundation pit of the steel bracing system. Thus, the Verhulst prediction model and the confidence interval security early forewarning model could be used to judge the stability of the steel bracing system.

The stability of a steel bracing system is one of the most important indicators by which to measure the stability of the foundation pit. Although a steel bracing system can be monitored using the axial force data, it is subject to the risk of sudden changes due to certain factors and has the characteristics of dynamic discontinuity. Therefore, safety risk management should be applied to the mutability of steel bracing systems to ensure stability. We used the steel bracing system of a foundation pit in Ningbo City, China, as the research object. First, through fault tree analysis (FTA) of the characteristics of the steel bracing system, a safety risk identification of the catastrophe risk was carried out. Second, the safety risk was standardized, and a safety risk evaluation index system was established; the validity and stability of the index system were then verified. Next, a catastrophe theory-based evaluation model was established to determine the safety risk level of the steel bracing system. Finally, a steel bracing risk management process was designed, and a safety risk response was proposed based on the results of the evaluation model. The result was that the membership function value belongs to the general risk area, which accords with the actual state of steel bracing in engineering, and subsequent safety risk response measures can be applied. The proposed method performs well in identifying the risk of sudden changes in steel bracing systems; it also rates the risk and offers response measures to ensure the stability of the steel bracing system. Additionally, this method is of significance due to its applicability in various projects.

This paper investigates a novel optimal control scheme for affine nonlinear systems. With the complexity of Hamilton-Jacobi-Bellman function and the linear differential inclusion based on neural network model is used to linearize the affine system, Meanwhile, a data-driven policy iterative algorithm is proposed to solve the relevant optimal solution. Finally, a simulation example is given to demonstrates the feasibility and applicability of the designed algorithm.