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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. [ABSTRACT FROM AUTHOR]

Combining the advantages of numerical simulation with experimental testing, real-time dynamic substructure (RTDS) testing provides a new experimental method for the investigation of engineered structures. However, not all unmodeled parts can be physically tested, as testing is often limited by the capacity of the test facility. Model updating is a good option to improve the modeling accuracy for numerical substructures in RTDS. In this study, a model updating method is introduced, which has great performance in describing this nonlinearity. In order to determine the optimal parameters in this model, an Unscented Kalman Filter (UKF)-based algorithm was applied to extract the knowledge contained in the sensors data. All the parameters that need to be identified are listed as the extended state variables, and the identification was achieved via the step-by-step state prediction and state update process. Effectiveness of the proposed method was verified through a group of experimental data, and results showed good agreement. Furthermore, the proposed method was compared with the Extended Kalman Filter (EKF)-based method, and better accuracy was easily found. The proposed parameter identification method has great applicability for structural objects with nonlinear behaviors and could be extended to research in other engineering fields. [ABSTRACT FROM AUTHOR]

A focus on environmental sustainability during the last decade has led to greater use of recycled aggregates to reduce the exploitation of existing reserves. In addition, environmentally consciousness has led to an increased use of renewable energy. Biomass is an important source of energy, and it also leads to high generation of waste products from biomass combustion. Previous studies have investigated the use of recycled aggregates from construction and demolition in road bases and sub-bases with positive results. This paper aims to study the feasibility of incorporating biomass bottom ash produced in the combustion process mixed with natural aggregates and recycled aggregates for use in civil engineering. The analysis of the mechanical properties was positive, showing the ability to use biomass bottom ash mixed with natural or recycled aggregates in specified ratios. Valuing biomass bottom ash can reduce waste generation and may lead to economic and environmental benefits. [ABSTRACT FROM AUTHOR]

In this paper, recent research and application activities on smart sensing, monitoring, and damage detection for civil infrastructures are briefly introduced. Emphasis is given to the activities in Korea. First, the state of the art in smart sensors technology is reviewed including optical fiber sensors, piezoelectric sensors, and wireless sensors. Then, a brief overview is given to the recent advances in the structural monitoring/damage detection techniques such as ambient vibration-based bridge health evaluation, piezoelectric sensors-based local damage detection, wireless sensor networks and energy harvesting, and wireless power transmission by laser/optoelectronic devices. Finally, recent collaborative R&D activities on smart structure technologies in Korea are discussed, which have been carried out on test-road bridges, cable-stayed bridges, and railroad bridges, sharing the up-to-date information and promoting the smart sensors and monitoring technologies for applications to civil infrastructures. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate the feasibility of an impedance-based damage detection technique using piezoelectric (PZT) transducers for civil infrastructures such as steel bridges. The basic concept of the technique is to monitor the changes in the electrical impedance to detect structural damages. Those changes in the electrical impedance are due to the electro-mechanical coupling property of piezoelectric materials and the host structure. In this study, at first, a numerical analysis was performed to understand the basics of this technique through a simple 1-D electro-mechanical system. The experimental studies on three kinds of structural members were carried out to detect the locations of cracks and loosened bolts. It was that cracks or loosened bolts near the PZT sensors could be effectively detected by monitoring the shifts of the resonant frequencies of the impedance functions. [ABSTRACT FROM AUTHOR]