Lixin Wang

Large-span stadium roofs in coastal regions are highly vulnerable to typhoon-induced wind damage, and their post-event performance depends on both structural safety and functionality recovery. This study proposes a probabilistic framework to assess typhoon-induced damage, functionality degradation, recovery, and resilience of a large-span stadium roof system in Shenzhen, China. Progressive damage to the roof cover and the roof-supporting structure is evaluated by combining wind tunnel pressure data and structural analysis. The results show that the roof cover shows greater vulnerability than the supporting structure, with slight damage emerging at around 30 m/s, whereas structural damage requires higher wind speeds. A functionality-based recovery model is further developed by considering repair preparation, repair duration, and repair sequence constraints. The building generally exhibits a high resilience level, with a mean resilience index of 0.9550 and a median of 0.9589. The initial overall building functionality loss increases from about 7% under TY conditions to 20% under STY and 60% under Super TY, while the recovery duration increases by about 2–3 times and 5–6 times relative to the TY case, respectively. The proposed framework provides a practical basis for resilience-oriented performance assessment of large-span roof structures under typhoon hazards.

A multi-objective tuning framework for optimizing Tuned Mass Damper (TMD) systems is presented. This framework optimizes the controlling parameters of TMDs while considering building resilience. The employed optimizer is the multi-objective HBA. Since TMDs are used in tall structures to mitigate seismic-induced structural responses, the proposed framework must be applicable to real-world scenarios; therefore, it determines both the placement and parameters of TMDs by accounting for the effects of various soil types and multiple earthquake records. Based on the obtained results, the optimal TMDs achieved an average roof-displacement reduction of 17% in soft soil, 7% in fixed-base conditions, and only 4% in dense soil, highlighting the decisive influence of soil–structure interaction on system efficiency. Moreover, there was considerable outcome variability across different earthquake records—ranging from 0.8% to 26% reduction—along with the observed negative effect (response amplification of up to 13.9% in certain fixed-base cases), which occurs when the TMD becomes detuned relative to the dominant frequency of the specific ground motion. This confirms the necessity for a robust design approach that simultaneously considers an ensemble of ground motions rather than optimizing for a single record.

Real-time monitoring of vessel navigation conditions in bridge waterways is essential for alerting to potential vessel–bridge collisions, which can be achieved by tracking a vessel’s trajectory in video, and fusing it with the complementary physical position information from the automatic identification system (AIS) to measure the position of the vessel. However, adverse weather conditions (e.g., foggy and rainy) would degrade video visibility, while occlusion could lead to losing the vessel’s visual features, causing inaccurate or failed tracking. To tackle these challenges, a novel monitoring method weather and occlusion enhanced vessel tracking (WOE-VesselTrack) is proposed. It consists of three components: 1) the recent deep learning algorithm DeepSORVF is adopted as a pipeline to extract and fuse the video and AIS trajectory data of a vessel, realizing a basic tracking function; 2) a degradation-type guided image restoration (DGIR) model is constructed to improve the visual quality of weather-degraded video. DGIR learns to predict the residual between the degraded image and the clear image with the guidance of weather type by supervised training, achieving video quality improvement adaptively; and 3) an adaptive triggering and Gaussian smooth interpolation (AT-GSI) algorithm is proposed to enhance the occluded trajectory prediction. It triggers the anti-occlusion operation adaptively based on the changes in detected vessel boxes and predicts the occluded position using nonlinear fitting historical trajectories. The proposed WOE-VesselTrack is validated on the benchmark dataset FVessel. The results show notable tracking accuracy improvements in the metric MOTA, with an average increase of 17.4% by incorporating DGIR and 4.3% by integrating AT-GSI. These significantly reduce tracking failures and improve trajectory precision, providing reliable vessel status information for the vessel–bridge collision warning system.

To enhance the seismic resilience of structures, this study proposes an optimization method for hybrid isolation systems based on a Seismic Resilience Enhancement Index (SREI). This index integrates the peak inter-story drift ratio and peak floor acceleration, considering multiple performance-related parameters such as repair cost, repair time, and casualty risk, addressing the limitations of traditional single-metric strategies. A four-story steel frame building, isolated by a hybrid system incorporating Magnetorheological (MR) dampers and a multi-component isolation bearing, is selected as the case study. Structural response data are obtained through finite element simulations, and a feedforward neural network is developed to establish a rapid prediction model for key performance indicators (SREI and isolation layer displacement). Multi-objective optimizations are conducted using the FNN-based surrogate model to obtain Pareto front solutions that minimize isolation layer displacement while maximizing SREI. Results show that the optimized design reduces the base layer displacement by 49.90% compared to the conventional isolation system, effectively controls the responses of the upper structure, and upgrades the overall seismic resilience grade. This study demonstrates the effectiveness of the SREI-based approach in resilience-oriented structural design and provides a data-driven pathway for parameter optimization of high-performance isolation systems.

Cities are complex systems influenced by a multitude of factors, encompassing society, economy, culture, and environment. These factors make urban development highly vulnerable to various disturbances. Communities work as the fundamental building blocks of a city and directly impact both its social structure and spatial layout. Moreover, urban planning and policies play a crucial role in shaping the development trajectory of communities and the living environment for residents. This study aims to develop a Bayesian and analytic hierarchy process (BAHP)-based multilevel community resilience evaluation method to assess the ability of the community system to withstand disturbances and recover from them. First, the proposed method establishes a comprehensive assessment index system that can evaluate social and environmental resilience as well as institutional and managerial resilience at multiple levels. This system serves as a quantitative decision-making tool to elucidate the impact of various factors on community resilience. Furthermore, the “relative demand coefficient” (RDC) is proposed to compare different communities’ resilience by using Bayesian inference to determine its most probable value (MPV). To validate the applicability of the proposed method, an empirical study was conducted in the Dafapu community located in the Longgang District of Shenzhen. Meanwhile, a simulated virtual community is employed for comparison with the Dafapu community as an illustrative example showcasing the proposed method’s superior performance after integrating the RDC. The empirical study demonstrates that the proposed BAHP-based method can effectively and quantitatively highlight the recovery capabilities and limitations for different communities in various dimensions while providing a clear direction for enhancing urban community resilience. This research contributes new insights to the theory, provides a practical tool to quantify community resilience, and offers a viable path for the actual enhancement of community resilience.

As urbanization accelerates, deep excavation projects have become increasingly vital in the construction of high-rise buildings and underground facilities. However, the potential risks to the surrounding environment and the inherent complexities involved necessitate thorough research to ensure the safety of those engineering projects with deep foundation pit excavation and to minimize their impact on adjacent structures. This study introduces a multi-stage and multi-parameter numerical simulation method to scrutinize the construction process of deep foundation pits. This approach not only investigates the influence of excavation activities on nearby buildings and roads but also enhances the fidelity of simulation models by establishing a three-dimensional finite element model integrated with on-site investigated geological information. Therefore, the proposed method can provide a more holistic and accurate analysis of the overall impacts of the pit excavation process. To examine the feasibility and effectiveness of the proposed method, this study adopts the multi-stage and multi-parameter influence analysis approach for a real practical engineering case to explore the impact of excavation on the foundation pit support structure, nearby buildings, and surrounding roads. The foundation pit support’s maximum displacement was 8.64 mm, well under the 25 mm standard limit. Anchor rod forces were about 10% below the standard limit. Building and road settlements were also minimal, at 10.33 mm and 16.44 mm, respectively, far below their respective limits of 200 mm and 300 mm. This study not only validates the feasibility of design and construction stability of deep foundation pits but also contributes theoretical and practical insights, serving as a valuable reference for future engineering projects of a similar scope.

Advanced composite materials have excellent performance and broad engineering application prospects, and have received widespread attention in recent years. Advanced composite materials can mainly be divided into fiber-reinforced composite materials, laminated composite materials, matrix composite materials, and other composite materials. This article provides a comprehensive overview of the types and characteristics of advanced composite materials, and provides a comprehensive evaluation of the latest research on structural strengthening and resilience improvement in advanced composite materials from the perspectives of new methods, modeling optimization, and practical applications. In the field of fiber-reinforced composite materials, the hybrid technology of carbon fiber and glass fiber can achieve dual advantages in combining the two materials. The maximum increase in mechanical properties of multilayer sandwich RH plate by hybrid technology is 435.4% (tensile strength), 149.2% (flexural strength), and 110.7~114.2% (shear strength), respectively. In the field of laminated composite materials, different mechanical properties of laminated composite materials can be obtained by changing the deposition sequence. In the field of matrix composites, nano copper oxide particles prepared by nanotechnology can increase the hardness and tensile strength of the metal matrix material by 77% and 78%, respectively. In the field of other composite materials, viscoelastic materials and magnetorheological variants have received widespread attention. The development of composite materials benefits from the promotion of new methods and technologies, but there are still problems such as complex preparation, high cost, and unstable performance. Considering the characteristics, application requirements, cost, complexity, and performance of different types of composite materials, further improvements and innovations are needed in modeling and optimization to better meet practical engineering needs, such as the application of advanced composite materials in civil engineering, ships, automobiles, batteries, and other fields.

Suffering from structural deterioration and natural disasters, the resilience of civil structures in the face of extreme loadings inevitably drops, which may lead to catastrophic structural failure and presents great threats to public safety. Earthquake-induced extreme loading is one of the major reasons behind the structural failure of buildings. However, many buildings in earthquake-prone areas of China lack safety monitoring, and prevalent structural health monitoring systems are generally very expensive and complicated for extensive applications. To facilitate cost-effective building-safety monitoring, this study investigates a method using cost-effective MEMS accelerometers for buildings’ rapid after-earthquake assessment. First, a parameter analysis of a cost-effective MEMS sensor is conducted to confirm its suitability for building-safety monitoring. Second, different from the existing investigations that tend to use a simplified building model or small-scaled frame structure excited by strong motions in laboratories, this study selects an in-service public building located in a typical earthquake-prone area after an analysis of earthquake risk in China. The building is instrumented with the selected cost-effective MEMS accelerometers, characterized by a low noise level and the capability to capture low-frequency small-amplitude dynamic responses. Furthermore, a rapid after-earthquake assessment scheme is proposed, which systematically includes fast missing data reconstruction, displacement response estimation based on an acceleration response integral, and safety assessment based on the maximum displacement and maximum inter-story drift ratio. Finally, the proposed method is successfully applied to a building-safety assessment by using earthquake-induced building responses suffering from missing data. This study is conducive to the extensive engineering application of MEMS-based cost-effective building monitoring and rapid after-earthquake assessment.

Impulse response function (IRF) is an ideal structural damage index for the identification of structural damage associated with changes in modal properties. However, IRFs from multiple excitations applied at different degrees-of-freedoms jointly contribute to the dynamic response, and their estimation is often underdetermined. Although some efforts have been devoted to the estimation of IRF for a structure under single excitation, the case under multiple excitations has not been fully investigated yet. The estimation of IRF under multiple excitations is generally an ill-conditioned inverse problem such that an incorrect or non-feasible solution is common, preventing its application to damage detection. This work explores this problem by introducing dimensionality reduction transformation matrices relating two sets of IRFs of a structure with discussions on the performance of the non-unique transformation matrices. Then, the extraction of IRF via wavelet-based and Tikhonov regularization-based methods are compared. Finally, a numerical study with a truss structure is conducted to validate the estimation of the IRFs and to demonstrate their applicability for damage detection under seismic excitations. Both the damage locations and severity are accurately identified, indicating the proposed methodology can enable the IRFs estimation under multiple excitations for successful damage detection.