SEISMIC DESIGN OF CROSS-LAMINATED TIMBER BUILDINGS

The increasing interest in cross-laminated timber (CLT) construction has resulted in multiple international research projects and publications covering the manufacturing and performance of CLT. Multiple regions and countries have adopted provisions for CLT into their engineering design standards and building regulations. Designing and building CLT structures, also in earthquake-prone regions is no longer a domain for early adopters, but is becoming a part of regular timber engineering practice. The increasing interest in CLT construction has resulted in multiple regions and countries adopting provisions for CLT into their engineering design standards. However, given the economic and legal differences between each region, some fundamental issues are treated differently, particularly with respect to seismic design. This article reflects the state-of-the-art on seismic design of CLT buildings including both, the global perspective and regional differences comparing the seismic design practice in Europe, Canada, the United States, New Zealand, Japan, China, and Chile

Structural health monitoring of underground metro tunnel by identifying damage using ANN deep learning auto-encoder

Due to the complexity of underground environmental conditions and operational incidents, advanced and accurate monitoring of the underground metro shield tunnel structures is crucial for maintenance and the prevention of mishaps. In the past few decades, numerous deep learning-based damage identification studies have been conducted on aboveground civil infrastructure. However, a few studies have been conducted for underground metro shield tunnels. This paper presents a deep learning-based damage identification study for underground metro shield tunnels. Based on previous experimental studies, a numerical model of a metro tunnel was utilized, and the vibration data obtained from the model under a moving load analysis was used for the evaluation. An existing deep auto-encoder (DAE) that can support deep neural networks was utilized to detect structural damage accurately by incorporating raw vibration signals. The dynamic analysis of a metro tunnel FEM model was conducted with different severity levels of the damage at different locations and elements on the structure. In addition, root mean square (RMS) was used to locate the damage at the different locations in the model. The results were compared under different schemes of white noise, varying levels of damage, and an intact state. To test the applicability of the proposed framework on a small dataset, the approach was also utilized to investigate the damage in a simply supported beam and compared with two deep learning-based methods (SVM and LSTM). The results show that the proposed DAE-based framework is feasible and efficient for the damage identification, damage size evaluation, and damage localization of the underground metro shield tunnel and a simply supported beam with comparison of two deep models

Structural Health Monitoring of Underground Metro Tunnel by Identifying Damage Using ANN Deep Learning Auto-Encoder

Due to the complexity of underground environmental conditions and operational incidents, advanced and accurate monitoring of the underground metro shield tunnel structures is crucial for maintenance and the prevention of mishaps. In the past few decades, numerous deep learning-based damage identification studies have been conducted on aboveground civil infrastructure. However, a few studies have been conducted for underground metro shield tunnels. This paper presents a deep learning-based damage identification study for underground metro shield tunnels. Based on previous experimental studies, a numerical model of a metro tunnel was utilized, and the vibration data obtained from the model under a moving load analysis was used for the evaluation. An existing deep auto-encoder (DAE) that can support deep neural networks was utilized to detect structural damage accurately by incorporating raw vibration signals. The dynamic analysis of a metro tunnel FEM model was conducted with different severity levels of the damage at different locations and elements on the structure. In addition, root mean square (RMS) was used to locate the damage at the different locations in the model. The results were compared under different schemes of white noise, varying levels of damage, and an intact state. To test the applicability of the proposed framework on a small dataset, the approach was also utilized to investigate the damage in a simply supported beam and compared with two deep learning-based methods (SVM and LSTM). The results show that the proposed DAE-based framework is feasible and efficient for the damage identification, damage size evaluation, and damage localization of the underground metro shield tunnel and a simply supported beam with comparison of two deep models

SEISMIC DESIGN OF CROSS-LAMINATED TIMBER BUILDINGS

The increasing interest in cross-laminated timber (CLT) construction has resulted in multiple international research projects and publications covering the manufacturing and performance of CLT. Multiple regions and countries have adopted provisions for CLT into their engineering design standards and building regulations. Designing and building CLT structures, also in earthquake-prone regions is no longer a domain for early adopters, but is becoming a part of regular timber engineering practice. The increasing interest in CLT construction has resulted in multiple regions and countries adopting provisions for CLT into their engineering design standards. However, given the economic and legal differences between each region, some fundamental issues are treated differently, particularly with respect to seismic design. This article reflects the state-of-the-art on seismic design of CLT buildings including both, the global perspective and regional differences comparing the seismic design practice in Europe, Canada, the United States, New Zealand, Japan, China, and Chile