In this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

This study evaluates the earthquake-induced movement of mechanically stabilized earth (MSE) walls. A thorough investigation was conducted on an MSE wall model, utilizing a comprehensive finite element (FE) analysis. This research focuses on investigating and designing MSE walls made of reinforcement concrete and hollow precast concrete panels. It also involves comparative studies such as on the vertical pressure of the wall, horizontal pressure of the wall, lateral pressure of the wall, settlement of the wall, settlement of the backfill reinforcement, vertical pressure of the backfill, horizontal pressure of the backfill, lateral pressure of the backfill, vertical settlement of the foundation, and settlements of soil layers across the height of the MSE walls. The FE simulations used a three-dimensional (3D) nonlinear dynamic FE model of full-scale MSE walls. The seismic performance of MSE walls has also been examined in terms of wall height. It was found that the seismic motion significantly impacts the height of the walls. In addition, the validity of the proposed study model was assessed by comparing it to the reinforcement concrete wall and ASSHTO guidelines using finite element (FE) simulation results. Based on the findings, the hollow prefabricated MSE wall was the most practical alternative due to its lower displacement and settlement. The specifics of the modeling approach used in this study and the lessons learned serve as benchmarks for future comparable lines of inquiry and practitioners, especially as the computational power of desktop computers continues to rise.

For the composite random reliability problem, based on the Markov hypothesis of the dynamic response spanning action, two procedures of conditional probability explanation are accomplished: to derive the 2nd-order approximate expression for the calculation of the dynamic reliability of the...

This article presents a scientometric review on the utilization of copper slag (CS) as a substitute constituent in ordinary Portland cement concrete, with a greater focus on analyzing CS as a supplementary cementitious material (SCM). The review was conducted through a comprehensive scientometric analysis of literature using Scopus and VOSviewer, examining publication trends, document types, subject areas, leading contributors, and the overall progression of research on CS concrete. The analysis revealed a substantial increase in publications between 2015 and 2022, with the journal “Construction and Building Materials” and the country “India” identified as the most influential in the field. The methodology involved filtering relevant documents to focus on the most impactful research, which was then critically analyzed to assess the fresh and hardened properties of CS concrete. The findings indicate that incorporating 5–10% of CS as an SCM can significantly enhance the mechanical properties and durability of concrete. CS was also found to improve concrete durability by imparting a micro-filler effect, thereby densifying the structure. Additionally, CS contributes to ecological benefits by incorporating heavy metals into the concrete matrix, preventing their leaching, and aiding in environmental conservation. Despite these promising results, the review acknowledges that the long-term performance of CS concrete remains a critical area that needs further investigation.