The construction of structures using cross-laminated timber (CLT) has grown in popularity as a result of its environmentally friendly and high-strength characteristics. The primary function of angle bracket connections is to resist the force of CLT structures under horizontal forces, which is essential to ensure the seismic resilience and ductility of CLT structures. A regression algorithms-driven method for predicting the mechanical performance of angle bracket connections is introduced in this study. As input parameters, the geometric dimensions of the angle bracket connector, the connection method of the connector with the wall and floor slabs, and the properties of the CLT panel are utilized to predict the yield load, the maximal load, the initial stiffness, and the ductility ratio of the angle bracket connection. Prediction models were developed using the collected data from 110 angle bracket experiments, and each prediction model's performance was discussed in depth. Lastly, the permutation importance and SHapley Additive exPlanations (SHAP) value analysis were used to interpret the prediction models. The results showed that the extreme gradient boosting (XGB) algorithm could accurately predict the maximum and yielding load of the angle bracket connection, with R² reaching 0.968 and 0.939. Furthermore, in predicting the initial stiffness of the angle bracket, the XGB algorithm performed the best with an average ratio of predicted to actual values of 0.985. The results indicated that this study proposed an accurate and efficient method for angle bracket connection to predicting its mechanical properties and confirmed the trustworthiness and feasibility of the prediction models.

Timber is susceptible to environmental humidity variations, inevitably resulting in cracks parallel to the wood grain during the service life. Cracks significantly degrade the effective cross-sectional area and seriously affect structural safety and durability. Therefore, it is significant to identify the timber elements’ cracking conditions for providing reliable maintenance. Existing timber structure crack inspection mainly relies on manual work. However, with the rapid development of high-rise and large-span glued timber structure, manual-based crack inspection is not applicable to such structures for increasing workload and uncontactable high-altitude timber elements. In order to make up for the deficiencies of the existing crack detection algorithms, this paper proposed an innovative computer vision-based method inspecting full-scale timber column cracks. In step one, the crack images were stitched to exhibit the full-scale cracking condition. In step two, the YOLOv5 model was trained utilizing 425 images collected from cracked timber structures and performed K -fold crossover validation algorithm. In step three, cracking regions are quantified at the physical level. Field tests showed that the proposed method has a crack identification precision better than 0.2 mm and error below 5% compared with manual measurement, which can provide high-precision, time-saving, and noncontact in-situ crack inspection for timber structures.

Vibrational response identification of high-rise building structures under excitation of varying ambient conditions is of great significance for structural vibration control and health monitoring. Traditional strategies allocating time-frequency domain transformation or graph neural network for feature extraction cast great loss on computational efficacy and engineering applicability. Herein, this study proposes a novel vibration recognition method based on feature extraction by deep autoencoder. In this method, a deep autoencoder (AUE) is established for vibration signal reconstruction in frequency domain, on the basis of which the vibration sensitive parameters are defined and quantified based on the discrepancy between original signals and the reconstructed ones. The extracted indicators are then performed as input feature engineering of downstream linear classifiers in a supervised pattern, thereby achieving rapid identification of vibration response. To validate the capacity of the proposed method, a task of vibration response identification of a super high-rise building under typical ambient excitations was conducted. The performance results achieved a satisfactory accuracy exceeding 0.95, suggesting a powerful capability and potential applicability.