Monitoring the mechanical behaviors of cross-laminated timber (CLT) connections is of great importance to the condition assessment of timber structures. To date, numerous research works have demonstrated that Lead Zirconate Titanate (PZT)-enabled active sensing approaches can achieve structural healthy state monitoring under monotonic loads, whereas their effectiveness for reciprocating loads still needs to be further studied. Moreover, traditional PZT-enabled active sensing approaches depend on prior knowledge and human judgment, restricting their field applications. Based on the above background, this research proposes an innovative method to monitor the mechanical behaviors of CLT connections under reciprocating loading by integrating PZT-enabled active sensing and eight machine learning (ML) approaches. Meanwhile, a new damage index based on wavelet packet decomposition and multiple signal path fusion is designed to improve the performance of ML methods. Finally, cyclic loading tests on CLT connections are conducted to demonstrate the outstanding capabilities of the proposed method than conventional PZT-enabled active sensing approaches.

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.

Manual geometric modeling of timber structures is time-intensive and error-prone, impeding efficient structural analysis. To overcome this limitation, this study develops an automated framework for the rapid generation of 3D geometric finite element (FE) models directly from LiDAR point clouds. The methodology first employs a region-growing algorithm for component segmentation. This is followed by the integration of geometric feature extraction techniques to robustly determine the position, orientation, boundaries, and dimensions of structural elements. The extracted geometric information is then output as an executable APDL (ANSYS Parametric Design Language) file for parametric geometric modeling, incorporating interfaces for customizing material and connection properties. The proposed framework accurately reconstructs geometries with high fidelity. It effectively addresses challenges arising from occlusions and incomplete point cloud data through boundary inference and contact relationship analysis. This approach demonstrates substantial promise for applications in both heritage conservation and modern timber engineering.

Carbonation depth is essential to determine the durability and predict the remaining service life of concrete structures. This study proposes a multi-frequency Rayleigh wave approximation method (MFRWA) to evaluate carbonation depth by exploiting the frequency-dependent penetration depths of ultrasonic Rayleigh waves. A series of numerical simulations are conducted to investigate the effective penetration depth of Rayleigh waves and the feasibility of the proposed MFRWA method on carbonation depth evaluation. Subsequently, the accelerated carbonation experiment is conducted to evaluate the carbonation depth using low-frequency and high-frequency Rayleigh waves, and the measured results from the Rayleigh wave method are compared with the ones from the phenolphthalein indicator and thermalgravimetric analysis (TGA) method. The results show that carbonation depth measured by Rayleigh wave method meets well with the one from TGA technique, demonstrating that the proposed method could provide a non-destructive and precise carbonation depth estimation. The proposed MFRWA method contributes a novel scheme for concrete carbonation evaluation and holds substantial potential in both laboratory and field applications.