Damage Detection and Condition Assessment of Civil Structures

The special collection on Damage Detection and Condition Assessment of Civil Structures is available in the ASCE Library (https://ascelibrary.org/page/jaeeez/damage_detection_condition_assessment_civil_structures).

Because of harsh environmental loads (e.g., traffic, wind, seismic, scour, or thermal effects), deterioration and degradation of infrastructures are always of great concern worldwide. A large proportion of current infrastructures are either structurally deficient or functionally obsolete. These deficient structures can potentially endanger their safety and economical use, and even develop into a structure failure if the damage is not detected at an early stage. The research on detecting damage at the earliest possible stage is universal throughout the fields of civil, mechanical, and aerospace engineering. Common damage detection methods include visual inspection; local nondestructive detection methods such as ultrasound, X-ray, dye penetrates, magnetic particle, and acoustic emission; and global detection methods such as vibration-based damage detection and statistical detection methods.

In the assessment of existing structures, engineers are increasingly faced with not only the challenges of early detection of damage, but also the evaluation of structure performance and behavior under damage, and economical and efficient retrofitting of the damaged components commonly found in older structures. In order to maintain the safety and integrity of structures, research on the damage mechanism, assessment of structure performance in damaged status, and innovative technologies and materials to rehabilitate, repair, and retrofit structures are of great significance and have become pervasive research topics.

The main focus of this special collection is to present recent advances in damage detection, health monitoring, structure condition assessment, and innovative applications in repair, retrofitting, and rehabilitation of structures. There are 35 articles collected in this special collection, which were published online from January 2018 to February 2019. In general, the included papers offer a diverse, international perspective from researchers worldwide. A broad range of topics have been covered by the collected papers and they can be generally grouped into four subject areas: structural and modal identification, structural damage detection and monitoring, structural condition assessment, and structural behavior under various loads.

On the subject of structural and modal identification, several papers focus on the identification of structure flexibility, frequency, modal property, stiffness, and railway system. Weng et al. (2018) investigate the identification of free–free flexibility for model updating and damage detection using the projection matrices perpendicular to rigid body motions to remove the rigid body components of flexibility of a free–free structure. Qu et al. (2018) investigate the frequency identification procedures for practical bridges to reduce the influence of noise through higher-order spectrum. Wen et al. (2018) investigate a method of combining analytical mode decomposition (AMD) and random decrement technique (RDT) to determine the modal properties of structures with closely spaced modes from ambient vibration data. Jiang et al. (2018) investigate a structural stiffness identification method for assessing traditional Chinese mortise-tenon joints based on a novel index. Zhu et al. (2018) investigate an adaptive regularization approach to identify the parameters of a railway ballasted track system (substructure) from dynamic measurements on in-service vehicles.

On the subject of structural damage detection and monitoring, various methods were proposed and studied by the authors. Liao et al. (2019) investigate a new damage detection and localization approach based on stochastic time series analysis, specifically using the damage-sensitive features extracted from vibration signals before and after a damage event. Pan et al. (2019) investigate a singular value decomposition (SVD)–based feature extraction method by designing a Hankel matrix to enhance multivariate analysis to improve data classification in machine learning. Wang et al. (2018a) investigate a damage identification technique based on the Laplace transform-based spectral element method (LTSEM) and strain statistical moment (SSM). Ghahremani et al. (2018) present a localized methodology for the automatic and systematic detection and quantification of damage in structural components using high-fidelity three-dimensional (3D) point cloud data, followed by a corresponding local update to a finite-element (FE) model. Xiong et al. (2018) investigate structural dynamical monitoring to identify the presence of bridge scouring through tracing the changes of vibration characteristics induced by scour. Huang et al. (2018) present a vibration-based nondestructive global damage identification method based on a genetic algorithm (GA) for the identification of structural damage location and severity under the influence of temperature variation and noise. Zhang et al. (2019) present a damage detection approach based on autoassociative neural networks (AANNs) to detect the structural damage in bridges by eliminating the temperature effects. Li et al. (2018b) present a study involving using an electromechanical impedance (EMI) technique to monitor early fiber-reinforced polymer (FRP) debonding in FRP shear-strengthened RC beams through experimental and numerical studies. Feng et al. (2019) experimentally investigate an active sensing approach integrated with piezoceramic-based transducers called smart aggregates (SAs) to estimate the strength development of the entire early-age concrete in real time. Wang et al. (2018b) present a method of structural stress monitoring based on piezoelectric (PZT) impedance frequency shift, which can monitor the health of the stressed structure. Kong et al. (2019) investigate a thin-film strain sensor, called a soft elastomeric capacitor (SEC) sensor, for sensing and monitoring fatigue cracks in steel bridges. Chang and Chou (2018) present a new damage detection method based on prediction errors using a bank of Kalman estimators. Damage was interpreted by statistical indexes from these errors and allowed determining the occurrence, levels, and locations of damage. Alzeyadi and Yu (2019) present the use of synthetic aperture radar (SAR) imaging for characterizing the subsurface moisture content and distribution using a concrete panel specimen as an example.

On the subject of structural condition assessment, the structure performance and health condition during in-service operation or after earthquakes were assessed using monitoring data. Wan and Ni (2019) investigate the use of a computationally efficient binary segmentation (BS) approach for change-point detection in order to classify and assess structural health condition. Al-Hussein and Haldar (2019) present a nondestructive rapid condition or health assessment procedure of three-dimensional civil structures following strong earthquakes. Chen et al. (2018) investigate structural safety evaluation of in-service tunnels based on an adaptive neuro-fuzzy inference system (ANFIS). Gunawardena et al. (2018) conducted purely data-based investigations to explore the feasibility of using several condition indicators to identify any changes occurring to the bridge condition.

A significant number of papers focused on the structure behavior under various loads including traffic, wind, seismic, and temperature loads. Han et al. (2018b) present an efficient approach for dynamic stress analysis of a long-span bridge under the combined action of stochastic traffic and wind loads based on the stress influence function. Hu et al. (2018) present an updated curved boundary transition section (BTS) to investigate the characteristics of wind fields at the bridge site in mountain-gorge terrains more accurately and rationally. Li et al. (2018a) conducted wind tunnel tests to investigate the wind pressure characteristics of sawtooth roofs in a simulated open country wind field with relatively low turbulence intensity. Ying et al. (2018) present a low-frequency continuous torsional motion technique (CTMT) to efficiently extract the aerostatic force coefficients of bridge decks. Li et al. (2018d) present an innovative time-variant dimension updating method (TVDUM) for random traffic and bridges to study the coupled vehicle and bridge dynamic interactions. Wang et al. (2018c) present a new approach for determining the truck weight limit of simply supported steel girder bridges based on the fatigue reliability of bridge girders under the action of random traffic flows considering the presence of multiple trucks. Han et al. (2018a) analyze the site-specific load characteristics of extra-heavy trucks (EHTs) based on a weigh-in-motion (WIM) data measure. Li et al. (2019) studied the dynamic performance of a strengthened concrete-filled steel tubular (CFST) arch bridge that has experienced serious damage from long-term heavy-truck action. Liu et al. (2018) present a new three-dimensional trans-scale crack growth model, developed from the microscale to macroscale, based on the concept of restraining stress zone, which aims to simulate the growth behaviors of fatigue cracks in a welded joint of orthotropic steel decks. Gajdoš et al. (2018) conducted a case study regarding the decrease in water pressure in a test pipe with dimensions of 530-mm outside diameter and 7.8-mm wall thickness. Li et al. (2018c) focused on the improvement of steel stiffened segments by investigating the failure reasons, mechanical behavior, and transmission efficiency. Peng et al. (2019) conducted pullout tests to investigate the influence of freeze-thaw cycles on the interfacial bond with ordinary concrete and concrete with high strength or additional frost resistance, aiming to increase the understanding of the durability of the bond between the near-surface-mounted carbon fiber–reinforced polymer (NSM CFRP) and concrete under freeze-thaw cycles. Lei et al. (2019) studied solar radiation effects on a curved bridge, investigating the transverse and vertical temperature differences in the box girder using three-dimensional finite-element analysis to achieve deeper understanding of temperature effects on curved concrete bridges with single-column piers.

The special issue guest editors would like to acknowledge Professor Wieslaw K. Binienda, editor in chief of the journal, for his support and encouragement for publishing this special issue, which made it a success and contributed to its high quality. In addition, this special collection would not have been possible without the support of the reviewers in providing timely and constructive comments during the peer-review process.