Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.

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.

An optimal sensor placement with multiple types of sensors could provide informative data of a structure to facilitate its structural damage detection. A response covariance-based multi-objective multi-type sensor optimal placement method has been thus developed. To validate this method, an experimental investigation was designed and performed in terms of a nine-bay three-dimensional frame structure, and the experimental details and results are presented in this article. The frame structure was first built, and a finite element model of the frame structure was constructed and updated. The proposed method was then applied to the finite element model to find the optimal sensor placement configuration. The multi-type sensors were then installed on the frame structure according to the determined optimal sensor numbers and positions. Different damage scenarios were then generated on the frame structure. These damage scenarios covered single and multiple damage cases occurring at different locations with different damage severities. A series of experiments, including the optimal and non-optimal sensor placements, were finally carried out, and the measurement data were used together with the finite element model to identify damage quantitatively. The identification results show that the optimal multi-type sensor placement determined by the proposed method could provide accurate damage localization and satisfactory damage quantitation and that the optimal sensor placement yielded better damage identification than the non-optimal sensor placement.

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.

Different types of sensors in a structural health monitoring (SHM) system installed in a structure enable various types of structural responses to be measured. However, their distinct properties and limitations considerably complicate multisensing structural condition assessment. As a result, the information from these sensors is often used separately, and the potential advantage of multisensing information has not been used effectively. This paper first proposes a covariance-based multisensing (CBMS) damage detection method in the time domain in terms of a CBMS vector as a new damage index and a sensitivity study for damage detection. The proposed method has the merit of assimilating heterogeneous data and reducing the adverse effect of measurement noise. The CBMS damage detection method is then used in two stages for detecting damage location and severity consecutively. Numerical studies are finally performed to investigate the feasibility and accuracy of the proposed framework using an overhang...