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This article discusses developing a methodology based on visual inspection for quantifying bridge damage (WABIM). The proposed methodology was developed through the application of weighted averages and a case study. Many current visual inspection methodologies, manuals, or guides related to bridges only allow qualitative results to be determined. Consequently, a high degree of inefficiency and inaccuracy was identified in the results from traditional methodologies; since they have a subjective approach, the results merely depend on the observer. Therefore, a methodological proposal was generated that allowed qualitative results to be described quantitatively, increasing the objectivity of the analysis and the accuracy of bridge maintenance plans. Rating ranges are used with weighted averages for each pathology, applied directly to the structural elements of the bridges. The classification guidelines and pathologies of bridge structures are adapted according to the Manual for the Visual Inspection of Bridges and Pontoons of Invías, Colombia. The case study was developed on a bridge in the city of Pereira, Colombia, presenting more significant surface deterioration and equipment deterioration. The WABIM methodology identified that periodic maintenance is required and the intervention's emphasis.

The existing micromechanical damage plasticity models assume that penny-shaped microcracks are present in the rocks, but it is seldom a reality. These models result in the abstract values of the damage variable. It is fundamentally due to the assumption of penny-shaped microcracks. To overcome this limitation, we have modified the micromechanical damage plasticity model such that the damage variable obtained from the proposed constitutive model relates to the measured damage from the recorded Acoustic Emission (AE) signals. This research presents a micromechanical coupled damage plasticity model by utilising AE data to predict the nonlinear mechanical response of intact rocks under Conventional Triaxial Compression (CTC) loading. The novelty of the proposed model is that it overcomes the limitation of penny-shaped microcracks and accounts for arbitrarily shaped microcracks by using a phenomenological approach. The applicability of the proposed model is shown by different rocks originating from varying geology. We have modelled the nonlinear mechanical behaviour of rock salt and coal (sedimentary rocks), tuff (soft igneous rock), and marble (hard metamorphic rock). The model performance is verified by comparing the results with the test data using the coefficient of determination (R2) statistical parameter. The proposed model predicts well the experimental data of damage variables with strain. It also successfully presents both strain hardening and softening behaviour of the rocks.HighlightsThe article contributes to the modification of the micromechanical coupled damage plasticity model such that the damage variable of the model relates to the experimentally obtained damage by Acoustic Emissions.The proposed model uses a phenomenological approach to account for arbitrarily shaped microcracks in the micromechanical coupled damage plasticity constitutive model.The proposed model is verified on different rocks with diverse geological genesis, such as rock salt, coal (sedimentary rocks), tuff (soft igneous rock), and marble (hard metamorphic rock) under uniaxial and triaxial compression loading conditions.

In this study, a magnetic flux leakage (MFL) method, known to be a suitable non-destructive evaluation (NDE) method for continuum ferromagnetic structures, was used to detect local damage when inspecting steel wire ropes. To demonstrate the proposed damage detection method through experiments, a multi-channel MFL sensor head was fabricated using a Hall sensor array and magnetic yokes to adapt to the wire rope. To prepare the damaged wire-rope specimens, several different amounts of artificial damages were inflicted on wire ropes. The MFL sensor head was used to scan the damaged specimens to measure the magnetic flux signals. After obtaining the signals, a series of signal processing steps, including the enveloping process based on the Hilbert transform (HT), was performed to better recognize the MFL signals by reducing the unexpected noise. The enveloped signals were then analyzed for objective damage detection by comparing them with a threshold that was established based on the generalized extreme value (GEV) distribution. The detected MFL signals that exceed the threshold were analyzed quantitatively by extracting the magnetic features from the MFL signals. To improve the quantitative analysis, damage indexes based on the relationship between the enveloped MFL signal and the threshold value were also utilized, along with a general damage index for the MFL method. The detected MFL signals for each damage type were quantified by using the proposed damage indexes and the general damage indexes for the MFL method. Finally, an artificial neural network (ANN) based multi-stage pattern recognition method using extracted multi-scale damage indexes was implemented to automatically estimate the severity of the damage. To analyze the reliability of the MFL-based automated wire rope NDE method, the accuracy and reliability were evaluated by comparing the repeatedly estimated damage size and the actual damage size.

Quantitatively and accurately monitoring the damage to composites is critical for estimating the remaining life of structures and determining whether maintenance is essential. This paper proposed an active sensing method for damage localization and quantification in composite plates. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection. The matching pursuit decomposition (MPD) algorithm was utilized to extract the precise TOF for damage detection. The damage localization was realized by comprehensively evaluating the damage probability evaluation results of all sensing paths in the monitoring area. Meanwhile, the scattering source was recognized on the elliptical trajectory obtained through the TOF of each sensing path to estimate the damage size. Damage size was characterized by the Gaussian kernel probability density distribution of scattering sources. The algorithm was validated by through-thickness hole damages of various locations and sizes in composite plates. The experimental results demonstrated that the localization and quantification absolute error are within 11 mm and 2.2 mm, respectively, with a sensor spacing of 100 mm. The algorithm proposed in this paper can accurately locate and quantify damage in composite plate-like structures.

Automatic detection of potholes is essential for effective road maintenance and is fundamental to enhancing environmental perception for intelligent transportation systems. Reducing false positives is essential for optimizing detection accuracy in this research domain. This paper introduces a novel method for detecting irregular potholes on road surfaces by integrating depth camera images with point cloud data. The proposed approach utilizes YOLOv8 for initial 2D object detection, identifying candidate regions and corresponding 3D point clouds. The boundary contours of potholes are subsequently determined through surface smoothness analysis, followed by the extraction of all point clouds within these boundaries. To further refine detection accuracy, elevation thresholds are applied to evaluate pothole depth, effectively filtering out false positives such as road surface stains and patches. The experiments were conducted over a 4.7-kilometer road section, demonstrating that on well-maintained road surfaces, the proposed method improves detection accuracy by compared to the standalone use of YOLOv8, achieving a precision of , a recall of , and an F1 score of . The model processes a single image in 0.23 seconds. Furthermore, the error rates for perimeter, surface area, and depth detection are limited to within , , and , respectively.

Summary Visual inspection procedures remain the primary method of infrastructure assessment throughout the USA, but their shortcomings are numerous. In addition to their widely acknowledged variability and subjectivity, the large scale of civil infrastructure systems presents expensive access and time requirements that constrain the frequency of visual inspections and result in poor temporal resolution, which hampers effective decision‐making. To overcome this challenge, the research reported herein aimed to assess the ability of computer algorithms together with imagery collected by unmanned aerial vehicles (UAV) to extract accurate and quantitative information to help inform infrastructure management decisions. Techniques such as homography and lens distortion correction are used in this article in a post‐processing framework that allows the use of color images obtained by UAVs for actual damage quantification measurements. The experiments described in this article utilize a UAV with a mounted camera and provide measurements from a representative infrastructure mockup with several simulated damage scenarios. Deformation measurements, change detection (related to structural features and the size of deterioration), and crack pattern identification were all analyzed. The results indicated that the developed post‐processing algorithms were able to extract quantitative information from UAV captured imagery. Copyright © 2016 John Wiley & Sons, Ltd.

Frontal collisions are among the most severe crash modes, requiring robust front-end design to ensure occupant safety. High-fidelity finite element (FE) simulations play a crucial role in evaluating energy absorption, intrusion patterns, and crash pulse fidelity during early vehicle design. This study develops and validates a high-fidelity finite-element model of a passenger-car BIW subjected to a 64 km/h full-frontal impact, addressing those limitations. The proposed framework couples global crash metrics energy balance, deceleration pulse, intrusion, and sectional forces with spatial plastic-strain mapping using shotgun plots to evaluate localized deformation behavior. Validation was performed against NCAP crash data and published finite-element models, showing close agreement: peak deceleration = 32 g (–1.5% error), pulse duration = 87 ms (–3.3%), and toe-pan intrusion = 123 mm (+ 2.5%). More than 92% of the initial kinetic energy was absorbed as internal plastic work with total energy balance error < 5%, confirming numerical stability. Shotgun analysis indicated that 68% of crash-box elements and 54% of front-rail elements exceeded the 0.15 strain threshold, identifying dominant energy-absorbing regions. The framework provides a reproducible, simulation-only approach for assessing crashworthiness and structural optimization without requiring experimental testing. The methodology can be readily extended to multi-condition crash scenarios and lightweight design applications, offering a cost-effective foundation for predictive safety evaluation in next-generation vehicle architectures.

The paper presents the novel method of damage identification and quantification in beams using the Wigner-Ville distribution (WVD). The presented non-parametric method is characterized by high sensitivity to a local stiffness decrease due to the presence of damage, comparable with the sensitivity of the wavelet-based approaches, however the lack of selection of the parameters of the algorithm, like wavelet type and its order, and the possibility of reduction of the boundary effect make this method advantageous with respect to the mentioned wavelet-based approaches. Moreover, the direct relation between the energy density resulting from the application of WVD to modal rotations make it possible to quantify damage in terms of its width and depth. The results obtained for the numerical modal rotations of a beam presented in this paper, simulating the results of non-destructive testing achievable with the shearography non-destructive testing method, confirm high accuracy in localization of a damage as well as quantification of its dimensions. It was shown that the WVD-based method is suitable for detection of damage represented by the stiffness decrease of 1% and can be identified and quantified with a high precision. The presented results of quantification allowed extracting information on damage width and depth.

A multistage damage detection method is introduced in this work that uses piezoelectric lead zirconate titanate (PZT) transducers to excite/sense the Lamb wave signals. A continuous wavelet transformation (CWT), based on the Gabor wavelet, is applied to accurately process the complicated wave signals caused by the damage. For a network of transducers, the damage can be detected in one detection cell based on the signals scattered by the damage, and then it can be quantitatively estimated by three detection stages using the outer tangent circle and least-squares methods. First, a single-stage damage detection method is carried out by exciting a transducer at the center of the detection cell to locate the damaged subcell. Then, the corner transducers are excited in the second and third stages of detection to improve the damage detection, especially the size estimation. The method does not require any baseline signal, and it only utilizes the same arrangement of transducers and the same data processing technique in all stages. The results from previous detection stages contribute to the improvement of damage detection in the subsequent stages. Both numerical simulation and experimental evaluation were used to verify that the method can accurately quantify the damage location and size. It was also found that the size of the detection cell plays a vital role in the accuracy of the results in this Lamb-wave-based multistage damage detection method.

In this study, acoustic emission (AE) analysis was carried out to evaluate and quantify the cracking behavior of large-scale multi-layered reinforced concrete beams under flexural tests. Four normal concrete beams were repaired by adding a layer of crumb rubberized engineered cementitious composites (CRECCs) or powder rubberized engineered cementitious composites (PRECCs), in either the tension or compression zone of the beam. Additional three unrepaired control beams, fully cast with either normal concrete, CRECCs, or PRECCs, were tested for comparison. Flexural tests were performed on all the tested beams in conjunction with AE monitoring until failure. AE raw data obtained from the flexural testing was filtered and then analyzed to detect and assess the cracking behavior of all the tested beams. A variety of AE parameters, including number of hits and cumulative signal strength, were utilized to study the crack propagation throughout the testing. Furthermore, b-value and intensity analyses were implemented and yielded additional parameters called b-value, historic index [H (t)], and severity (Sr). The analysis of the changes in the AE parameters allowed the identification of the first crack in all tested beams. Moreover, varying the rubber particle size (crumb rubber or powder rubber), repair layer location, or AE sensor location showed a significant impact on the number of hits and signal amplitude. Finally, by using the results of the study, it was possible to develop a damage quantification chart that can identify different damage stages (first crack and ultimate load) related to the intensity analysis parameters (H (t) and Sr).