Damage quantification and localization algorithms for indirect SHM of bridges

Bridges, crucial for surface transportation networks, carry continuous vehicle loads in varying weather conditions. Throughout the service life, bridges accumulate damage due to increased traffic, fatigue, harsh environmental conditions, and natural hazards. The accumulation of damage poses a potential fatality risk if not promptly monitored. Therefore, regular safety assessments and immediate post-disaster evaluations have become standard practice. In this context, the bridge weigh-in-motion (B-WIM) system emerges as a promising alternative to traditional bridge health monitoring (BHM) methods. B-WIM utilizes moving vehicle loads to estimate the bridge influence line (BIL), which provides critical structural information for effective BHM. A recent state-of-the-art review highlights ongoing research primarily focused on detecting and localizing damage along the span, rarely along the width, and with limited studies addressing damage quantification. However, localization and quantification of damage across the bridge width are seldom studied. This paper proposes an approach for novel localization of damage across the bridge width and quantification of damage using a relationship between flexural rigidity and deflection influence line (DIL). To achieve this, the concept of bridge influence surface (BIS), a 2D expansion of individual BILs across the bridge’s width, is utilized. Through a brief description of the B-WIM algorithm, this paper introduces the flexural rigidity estimation (FRE) method for damage quantification. In addition, a static-like DIL reconstruction is proposed to enhance robustness under dynamic bridge response. The proposed approach is initially verified with a 2D finite-element (FE) beam model, followed by a detailed simulation study using a full-scale 3D FE model of a real bridge. Results from comprehensive numerical analyses are further verified through experimental investigation on a scaled-down model of the same bridge. The findings demonstrate promising BIS-based damage localization across the width and FRE-based damage severity assessment. These insights contribute to the enhancement of BHM systems and outline potential avenues for future research.

Need for developing efficient non-destructive damage detection procedures for civil engineering structures is growing rapidly. This paper presents a methodology for detection and quantification of structural damage using modal information obtained from transfer matrix technique. Vibration characteristics of beam-like structure have been determined using the computer program developed based on the formulations presented in the paper. It has been noted from reported literature that detection and quantification of damage using mode shape information is difficult and further, extraction of mode shape information has practical difficulties and limitations. Hence, a methodology for detection and quantification of damage in structure using tranfer matrix technique based on the changes in the natural frequencies has been developed. With an assumption of damage at a particular segment of the beam-like structure, an iterative procedure has been formulated to converge the calculated and measured frequencies by adjusting flexural rigidity of elements and then, the intersections are used for detection and quantification of damage. Eventhough the developed methodology is iterative, computational effort is reduced considerably by using transfer matrix technique. It is observed that the methodology is capable of predicting the location and magnitude of damage quite accurately.

Damage diagnosis has become a valuable tool for asset management, enhanced by advances in sensor technologies that allows for system monitoring and providing massive amount of data for use in health state diagnosis. However, when dealing with massive data, manual feature extraction is not always a suitable approach as it is labor intensive requiring the intervention of domain experts with knowledge about the relevant variables that govern the system and their impact on its degradation process. To address these challenges, convolutional neural networks (CNNs) have been recently proposed to automatically extract features that best represent a system’s degradation behavior and are a promising and powerful technique for supervised learning with recent studies having shown their advantages for feature identification, extraction, and damage quantification in machine health assessment. Here, we propose a novel deep CNN‐based approach for structural damage location and quantification, which operates on images generated from the structure’s transmissibility functions to exploit the CNNs’ image processing capabilities and to automatically extract and select relevant features to the structure’s degradation process. These feature maps are fed into a multilayer perceptron to achieve damage localization and quantification. The approach is validated and exemplified by means of two case studies involving a mass‐spring system and a structural beam where training data are generated from finite element models that have been calibrated on experimental data. For each case study, the models are also validated using experimental data, where results indicate that the proposed approach delivers satisfactory performance and thus being an appropriate tool for damage diagnosis.

A complementary catadioptric imaging technique was proposed to solve the problem of low and nonuniform resolution in omnidirectional imaging. To enhance this research, our paper focuses on how to generate a high-resolution panoramic image from the captured omnidirectional image. To avoid the interference between the inner and outer images while fusing the two complementary views, a cross-selection kernel regression method is proposed. First, in view of the complementarity of sampling resolution in the tangential and radial directions between the inner and the outer images, respectively, the horizontal gradients in the expected panoramic image are estimated based on the scattered neighboring pixels mapped from the outer, while the vertical gradients are estimated using the inner image. Then, the size and shape of the regression kernel are adaptively steered based on the local gradients. Furthermore, the neighboring pixels in the next interpolation step of kernel regression are also selected based on the comparison between the horizontal and vertical gradients. In simulation and real-image experiments, the proposed method outperforms existing kernel regression methods and our previous wavelet-based fusion method in terms of both visual quality and objective evaluation.

In this paper, an efficient blind equalization with two‐stage single/multilevel modulus and decision directed (DD) least mean square (LMS)‐like algorithm is proposed for high‐order QAM (64/256/1024QAM) cable modem systems. In wire‐lined cable systems, it seems that the cable channel may display a nearly invariant multi‐path effect over a considerable time, and then the blind equalizer can have the capability to recover the received high‐order QAM symbols on wired cables. The proposed blind equalization algorithm applies a two‐stage convergence scheme, which results from the multi‐modulus algorithm (MMA), sometimes called the modified constant modulus algorithm (MCMA), and the DD LMS‐like method. In the first equalization stage, the joint MMA and DD LMS‐like methods are applied for the purpose of fast convergence. When the convergence process reaches an acceptably steady condition, the convergence detector changes the process into the second stage. In the second equalization stage, the enhanced DD LMS‐like method with the generalized multi‐modulus is applied to reduce the mean square error (MSE) of equalizations further. For high‐order QAM cable systems, the proposed method performs faster than the previous well‐known blind methods do. Simultaneously, the proposed algorithm also provides less MSE and lower symbol error rate (SER) than the other blind methods do at the same signal‐to‐noise ratio (SNR).

Damage Detection of Bridge Structures Using Modal Flexibility under Temperature Variations

Embedded Damage Localization Subsystem Based on Elastic Wave Propagation

In this study, a reference-free damage characterization technique is developed not only to identify but also to locate and quantify damage in composite structures subject to varying temperature conditions. First, damage is characterized in terms of a damage index (m-value) defined as the ratio of damage size to the wavelength of the A0 mode within the damage. Then, a feasible solution space defining all possible combinations of the damage location and size are estimated without using any prior baseline data obtained from the pristine condition of a structure or different paths. When additional information such as the A0 mode group velocity within the pristine region of the structure becomes available, the estimates for the damage location and size are updated with better accuracy. The uniqueness of this study lies in that damage localization and quantification as well as identification are all performed without comparing current Lamb wave signals with the ones obtained from the pristine condition of the target structure, making the proposed technique more attractive for online monitoring. Numerical and experimental tests are presented to demonstrate the effectiveness of the proposed damage detection technique under varying temperature.

Neural networks for 3D temperature field reconstruction via acoustic signals

High spatial resolution flaw detector based on the GMR eddy-current probe

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

Purpose Aiming at the problems of high cost, high risk and traffic impact of in situ load testing of Bridges after fire, this paper proposes a fire bridge structural damage identification method based on a fire dynamics simulator (FDS)-Kriging model modification and a unit modal strain energy damage index, so as to effectively assess the damage of bridge structures after fire. Determine whether it can continue to be used and whether it affects driving safety. Design/methodology/approach Propose a fire bridge structural damage identification method to establish the FDS-Kriging model correction. Findings The finite element model of the fire bridge modified using FDS-Kriging approach can reflect the actual situation of the bridge structure more accurately, and the damage location and degree identified by the modal strain energy method are consistent with the actual bridge. Therefore, the effectiveness of the proposed method has been verified, and a new strategy for fire bridge damage identification has been provided. Research demonstrates that the finite element model of the fire-damaged bridge, modified using the FDS-Kriging method, can more accurately reflect the actual structural state. Moreover, the damage location and severity identified via the modal strain energy method align well with the actual bridge conditions. Thus, the effectiveness and engineering applicability of the proposed method are validated, offering a comprehensive new strategy and technical support for the integrated performance evaluation, damage identification and reliability analysis of fire-damaged bridges. Originality/value By determining the finite element correction interval and combining it with the Kriging model, the method realizes the fine correction of the finite element model of the fire bridge. Structural damage identification is carried out by using the unit modal strain energy damage index. This verifies the effectiveness and engineering applicability of the proposed method, which offers a novel approach and technical support for the comprehensive performance evaluation, damage identification and reliability analysis of fire-damaged bridges.

Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

A novel damage localization technique for type III composite pressure vessels based on guided wave mode-matching method

Online damage quantification suffers from insufficient labeled data that weakens its accuracy. In this context, adopting the domain adaptation on historical labeled data from similar structures/damages or simulated digital twin data to assist the current diagnosis task would be beneficial. However, most domain adaptation methods are designed for classification and cannot efficiently address damage quantification, a regression problem with continuous real-valued labels. This study first proposes a novel domain adaptation method, the Online Fuzzy-set-based Joint Distribution Adaptation for Regression, to address this challenge. By converting the continuous real-valued labels to fuzzy class labels via fuzzy sets, the marginal and conditional distribution discrepancy are simultaneously measured to achieve the domain adaptation for the damage quantification task. Thanks to the superiority of the proposed method, a state-of-the-art online damage quantification framework based on domain adaptation is presented. Finally, the framework has been comprehensively demonstrated with a damaged helicopter panel, in which three types of damage domain adaptations (across different damage locations, across different damage types, and from simulation to experiment) are all conducted, proving the accuracy of damage quantification can be significantly improved in a realistic environment. It is expected that the proposed approach to be applied to the fleet-level digital twin considering the individual differences.