Accurately and dynamically predicting ground settlements during the construction of foundation pits is pivotal to the understanding of the potential risk of foundation pits and, therefore, enables constructors to take timely and effective actions to ensure the construction safety of foundation pits. Existing settlement prediction methods mainly focus on the prediction of the maximum ground settlements based on static influence factors, such as soil properties and the geometry of foundation pits. However, these methods are unable to be applied to the prediction of daily ground settlements in a direct way because daily ground settlements can be affected by many time-dependent influence factors, and an accurate prediction of daily ground settlements should take into consideration such factors. To address this problem, this paper proposes an artificial neural network-based daily ground settlement prediction method, where both static and time-dependent influence factors, as well as previous settlement monitoring data, are considered in the optimum artificial neural network. The proposed method is validated using data collected from a real cut-and-cover highway tunnel project in western Hangzhou, China. The results demonstrate that time-dependent influence factors and previous settlement monitoring data play vital roles in establishing an optimum artificial neural network for the accurate prediction of daily ground settlement.

Restoration of damaged highway-bridge networks is critical to the recovery of the networks’ functionality in support of post-disaster recovery actions. Restoration-scheduling problems have traditionally assumed that parallel restoration actions to different bridges on a highway segment can be undertaken simultaneously. However, such assumption may have unfavorable consequences insofar as, in real-world scenarios, downtime may result from impassability of a segment due to blockage arising from the preceding restoration actions to their subsequent ones on the same segment. Consequently, downtime due to interactions of parallel restoration actions may hinder effective recovery of networks’ functionality. Accordingly, this paper proposes an integer program to investigate the restoration interactions in post-earthquake highway–bridge networks and such interactions’ impacts on optimal restoration scheduling and networks’ resilience recovery processes. A hybrid genetic algorithm that combines a genetic algorithm with a specifically designed heuristic approach is developed to improve the proposed integer program’s computational efficiency. The results of a case study using the proposed method and data from the 2008 Wenchuan Earthquake show that the downtime due to restoration interactions can delay the recovery of the networks’ functionality, and thus that neglecting such interactions can lead to the overestimation of the networks’ resilience.

Assessing the seismic vulnerability of large numbers of buildings is an expensive and time-consuming task, requiring the collection of highly complex and multifaceted data on building characteristics and the use of sophisticated computational models. This study reports on the development of a data mining technique: Support Vector Machine (SVM) for resolving such multi-dimensional data problems for assessing buildings’ seismic vulnerability at a regional scale. Particularly, we developed an SVM model for rapid assessment of the macroscale seismic vulnerability of buildings in terms of spectral yield and ultimate points of their capacity curves. Two case studies, one with 11 building characteristics and the other with 20, were used to test the proposed SVM model. The results show that when 20 building characteristics are included, an individual building’s seismic vulnerability in term of its spectral yield and ultimate points can be predicted by the proposed SVM model with an average 64% accuracy if the training dataset contains 400 samples, rising to 74% with 4400 training samples. Coupling the proposed technique with demand curves based on buildings’ locations will enable rapid and reliable seismic-risk assessment at a regional scale, requiring only basic building characteristics rather than complex computational models.

CH₃NH₃PbI_3−xCl_xperovskite material has been commonly used as the free charge generator and reservoir in highly efficient perovskite-based solid-state solar photovoltaic devices.

Automatic object-detection technique can improve the efficiency of building data collection for semi-empirical methods to assess the seismic vulnerability of buildings at a regional scale. However, current structural element detection methods rely on color, texture and/or shape information of the object to be detected and are less flexible and reliable to detect columns or walls with unknown surface materials or deformed shapes in images. To overcome these limitations, this paper presents an innovative gray-level histogram (GLH) statistical feature-based object-detection method for automatically identifying structural elements, including columns and walls, in an image. This method starts with converting an RGB image (i.e. the image colors being a mix of red, green and blue light) into a grayscale image, followed by detecting vertical boundary lines using the Prewitt operator and the Hough transform. The detected lines divide the image into several sub-regions. Then, three GLH statistical parameters (variance, skewness, and kurtosis) of each sub-region are calculated. Finally, a column or a wall in a sub-region is recognized if these features of the sub-region satisfy the predefined criteria. This method was validated by testing the detection precision and recall for column and wall images. The results indicated the high accuracy of the proposed method in detecting structural elements with various surface treatments or deflected shapes. The proposed structural element detection method can be extended to detecting more structural characteristics and retrieving structural deficiencies from digital images in the future, promoting the automation in building data collection.

Buildings’ energy resilience in natural disasters is reliant on the support of the functionalities of critical infrastructure that the buildings connect to, such as highway-bridge and electric power systems. Meanwhile, as critical infrastructure systems have increasingly become interconnected and interdependent, they are more susceptible to natural hazards and less able to withstand their effects. Insufficient research has been conducted regarding computational models of effectively representing the interdependencies and interactions involved in the restoration scheduling of post-disaster critical infrastructure systems. To address this research gap, this study proposes integer programs, integrating hybrid genetic algorithms, to explicitly investigate the impact of interactions and interdependencies between electric power systems (EPSs) and highway-bridge systems (HBSs) on the energy-recovery processes of buildings. The objective is to dynamically prioritize the restoration scheduling for EPSs and HBSs while considering inspection and restoration activities. A case study based on the 2008 Wenchuan Earthquake in Sichuan province, China, is employed to validate the efficacy of the proposed method. The results of the analysis reveal that the dynamic model exhibits a substantial 6.4% improvement in building energy resilience at the seven-day mark, compared to the static model. Moreover, the proposed coupled EPS–HBS inspection–restoration joint model outperforms a disjoint EPS inspection–restoration scheduling model, yielding a remarkable 11.4% enhancement in system resilience at the seven-day mark. These findings underscore the significance of considering interdependencies and interactions within critical infrastructure systems to enhance the energy resilience of buildings in earthquake-affected areas.