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17 pages, 16055 KiB

Open AccessArticle

Displacement Field Calculation of Large-Scale Structures Using Computer Vision with Physical Constraints: An Experimental Study

by Yapeng Guo, Peng Zhong, Yi Zhuo, Fanzeng Meng, Hao Di and Shunlong Li

Sustainability 2023, 15(11), 8683; https://doi.org/10.3390/su15118683 - 27 May 2023

Cited by 1 | Viewed by 1372

Abstract

In recent years, computer vision-based structural displacement acquisition technique has received wide attention and research due to the advantages of easy deployment, low-cost, and non-contact. However, the displacement field acquisition of large-scale structures is a challenging topic as a result of the contradiction [...] Read more.

In recent years, computer vision-based structural displacement acquisition technique has received wide attention and research due to the advantages of easy deployment, low-cost, and non-contact. However, the displacement field acquisition of large-scale structures is a challenging topic as a result of the contradiction of camera field-of-view and resolution. This paper presents a large-scale structural displacement field calculation framework with integrated computer vision and physical constraints using only one camera. First, the full-field image of the large-scale structure is obtained by processing the multi-view image using image stitching technique; second, the full-field image is meshed and the node displacements are calculated using an improved template matching method; and finally, the non-node displacements are described using shape functions considering physical constraints. The developed framework was validated using a scaled bridge model and evaluated by the proposed evaluation index for displacement field calculation accuracy. This paper can provide an effective way to obtain displacement fields of large-scale structures efficiently and cost-effectively. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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19 pages, 6687 KiB

Open AccessArticle

Damage Detection of High-Speed Railway Box Girder Using Train-Induced Dynamic Responses

by Xin Wang, Yi Zhuo and Shunlong Li

Sustainability 2023, 15(11), 8552; https://doi.org/10.3390/su15118552 - 24 May 2023

Cited by 2 | Viewed by 2491

Abstract

This paper proposes a damage detection method based on the train-induced responses of high-speed railway box girders. Under the coupling effects of bending and torsion, the traditional damage detection method based on the Euler beam theory cannot be applied. In this research, the [...] Read more.

This paper proposes a damage detection method based on the train-induced responses of high-speed railway box girders. Under the coupling effects of bending and torsion, the traditional damage detection method based on the Euler beam theory cannot be applied. In this research, the box girder section is divided into different components based on the plate element analysis method. The strain responses were preprocessed based on the principal component analysis (PCA) method to remove the influence of train operation variation. The residual error of the autoregressive (AR) model was used as a potential index of damage features. The optimal order of the model was determined based on the Bayesian information criterion (BIC) criterion. Finally, the confidence boundary (CB) of damage features (DF) constituting outliers can be estimated by the Gaussian inverse cumulative distribution function (ICDF). The numerical simulation results show that the proposed method in this paper can effectively identify, locate and quantify the damage, which verifies the accuracy of the proposed method. The proposed method effectively identifies the early damage of all components on the key section by using four strain sensors, and it is helpful for developing effective maintenance strategies for high-speed railway box girders. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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17 pages, 51309 KiB

Open AccessArticle

Local Track Irregularity Identification Based on Multi-Sensor Time–Frequency Features of High-Speed Railway Bridge Accelerations

by Ye Mo, Yi Zhuo and Shunlong Li

Sustainability 2023, 15(10), 8237; https://doi.org/10.3390/su15108237 - 18 May 2023

Cited by 1 | Viewed by 1142

Abstract

Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train–track–bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities based on the [...] Read more.

Shortwave track diseases are generally reflected in the form of local track irregularity. Such diseases will greatly impact the train–track–bridge interaction (TTBI) dynamic system, seriously affecting train safety. Therefore, a method is proposed to detect and localize local track irregularities based on the multi-sensor time–frequency features of high-speed railway bridge accelerations. Continuous wavelet transform (CWT) was used to analyze the multi-sensor accelerations of railway bridges. Moreover, time–frequency features based on the sum of wavelet coefficients were proposed, considering the influence of the distance from the measurement points to the local irregularity on the recognition accuracy. Then, the multi-domain features were utilized to recognize deteriorated railway locations. A simply-supported high-speed railway bridge traversed by a railway train was adopted as a numerical simulation. Comparative studies were conducted to investigate the influence of vehicle speeds and the location of local track irregularity on the algorithm. Numerical simulation results show that the proposed algorithm can detect and locate local track irregularity accurately and is robust to vehicle speeds. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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23 pages, 8395 KiB

Open AccessArticle

Experimental and Numerical Investigation of the Anti-Overturning Theory of Single-Column Pier Bridges

by Hao Xu, Qiyuan Li, Dongcai Li, Haonan Jiang, Tong Wang and Qingfei Gao

Sustainability 2023, 15(2), 1545; https://doi.org/10.3390/su15021545 - 13 Jan 2023

Cited by 1 | Viewed by 1439

Abstract

In recent years, overturning accidents at single-pier bridges have occurred frequently, resulting in significant property losses and safety accidents. Overturning accidents show that there are still many hidden dangers in the design, operation, and management of existing single-pier bridges. Therefore, this paper takes [...] Read more.

In recent years, overturning accidents at single-pier bridges have occurred frequently, resulting in significant property losses and safety accidents. Overturning accidents show that there are still many hidden dangers in the design, operation, and management of existing single-pier bridges. Therefore, this paper takes the K503 + 647.4 separated overpass of the Hegang–Dalian Expressway as the research object and carries out an onsite anti-overturning stability test of a single-column pier bridge. Through loading under various working conditions, the displacement changes of each support are measured, and the reaction changes of the supports are calculated. In the process of simulating the field test using the finite element program ANSYS, a rigid model based on ideal support and an elastic model considering beam deformation are established, and the accuracy of the elastic model is verified by comparison with the field-measured data. Furthermore, a series of parameters, such as the bridge side-span ratio, bridge span number, bearing spacing, loading position, and torsional rigidity, are varied, and finite element simulation is carried out on the basis of the elastic model. Through comparison of the results, a relationship between the parameters of the single-pier bridge and the anti-overturning ability is obtained, which provides a theoretical basis for anti-overturning design research and the effective reinforcement of single-pier bridges. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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13 pages, 3375 KiB

Open AccessArticle

Experimental Study of Deformation Measurement of Bored Pile Using OFDR and BOTDR Joint Optical Fiber Sensing Technology

by Lei Gao, Jiben Qian, Chuan Han, Shiwei Qin and Kunpeng Feng

Sustainability 2022, 14(24), 16557; https://doi.org/10.3390/su142416557 - 9 Dec 2022

Cited by 4 | Viewed by 1266

Abstract

Pile foundation is the most common foundation form in geotechnical engineering; it is very important for engineering safety. In order to accurately grasp the deformation of pile foundation, OFDR (optical frequency domain reflectometer) and BOTDR (Brillouin optical time domain reflectometer) optical fiber sensing [...] Read more.

Pile foundation is the most common foundation form in geotechnical engineering; it is very important for engineering safety. In order to accurately grasp the deformation of pile foundation, OFDR (optical frequency domain reflectometer) and BOTDR (Brillouin optical time domain reflectometer) optical fiber sensing technologies are used to measure the strain variation of pile foundation. The measurement results of the two technologies are analyzed, and different data processing methods are used. The ability of the two methods to measure the strain of pile foundation is evaluated. The results show that OFDR technology can achieve high-precision and distributed measurement of strain of pile; BOTDR technology can achieve the monitoring effect of OFDR to a certain extent using appropriate data processing methods; the combination of the two methods can make up for the shortcomings of the short monitoring distance of the OFDR technique and the low accuracy of the BOTDR technique; by comparing the application effect with the two technologies in geotechnical engineering, the application prospect of OFDR–BOTDR joint optical fiber sensing technology in geotechnical engineering is discussed. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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19 pages, 3255 KiB

Open AccessArticle

Study on the Reliability Evaluation Method and Diagnosis of Bridges in Cold Regions Based on the Theory of MCS and Bayesian Networks

by Zhonglong Li, Wei Ji, Yao Zhang, Sijia Ge, Haonan Bing, Mingjun Zhang, Zhifeng Ye and Baowei Lv

Sustainability 2022, 14(21), 13786; https://doi.org/10.3390/su142113786 - 24 Oct 2022

Cited by 3 | Viewed by 1227

Abstract

The safety assessment of bridges in cold areas under the special environmental effects of extremely low temperatures, frequent freezing and thawing, and chloride ion erosion from snow removal with deicing salt, presents challenges that requiring solving. Thus, this paper proposes a new method [...] Read more.

The safety assessment of bridges in cold areas under the special environmental effects of extremely low temperatures, frequent freezing and thawing, and chloride ion erosion from snow removal with deicing salt, presents challenges that requiring solving. Thus, this paper proposes a new method of safety assessment based on a combination of Monte Carlo simulation (MCS) and Bayesian theory that achieves the reliability evaluation and reverse diagnosis of the overall safety performance of reinforced concrete bridges in cold areas. Additionally, the new method accomplishes the intelligent grading of various safety performance aspects of the bridge, which provides substantial references for the maintenance and reinforcement of in-service bridges. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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18 pages, 7924 KiB

Open AccessArticle

Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges

by Shaoyi Zhang, Yongliang Wang and Kaiping Yu

Sustainability 2022, 14(19), 12134; https://doi.org/10.3390/su141912134 - 25 Sep 2022

Cited by 3 | Viewed by 1380

Abstract

In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent structural damage. Environmental and operational loads cause nonstationarity in the strain monitoring data of urban girder bridges. Such nonstationary monitoring data can mask damage and reduce the [...] Read more.

In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent structural damage. Environmental and operational loads cause nonstationarity in the strain monitoring data of urban girder bridges. Such nonstationary monitoring data can mask damage and reduce the accuracy of the established baseline model. To address this problem, a steady-state data baseline model for bridges is proposed. First, for observable effects such as ambient temperature, a directional projection decoupling method for strain monitoring data is proposed, which can reduce the nonstationary effect of ambient temperature, and the effectiveness of this method is proven using equations. Second, for unobservable effects such as traffic load, a k-means clustering method for steady state of traffic loads is proposed; using this method, which can divide the steady and nonsteady states of traffic loads and reduce the nonstationary effect of traffic loads on strain monitoring data, a steady-state baseline model is established. Finally, the effectiveness of the steady-state baseline model is verified using an actual bridge. The results show that the proposed baseline model can reduce the error caused by nonstationary effects, improve the modelling accuracy, and provide useful information for subsequent damage identification. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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25 pages, 6885 KiB

Open AccessArticle

A Two-Stage Approach for Damage Diagnosis of Structures Based on a Fully Distributed Strain Mode under Multigain Feedback Control

by Zheng Zhou, Kaizhi Dong, Ziwei Fang and Yang Liu

Sustainability 2022, 14(16), 10019; https://doi.org/10.3390/su141610019 - 12 Aug 2022

Cited by 1 | Viewed by 1251

Abstract

The application of distributed fiber sensing technology in civil engineering has been developed to obtain more accurate and reliable information for structural health monitoring (SHM). With this sensing technique, high-density strain data are provided to benefit the stability and robustness in a closed-loop [...] Read more.

The application of distributed fiber sensing technology in civil engineering has been developed to obtain more accurate and reliable information for structural health monitoring (SHM). With this sensing technique, high-density strain data are provided to benefit the stability and robustness in a closed-loop damage detection method which has not yet been investigated. To address this concern, a two-stage approach for structural damage detection combining a modal strain energy-based index (MSEBI) method with a hybrid artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. In this study, the fully distributed strain measurement is taken advantage of, and a strain-based, closed-loop system with multiple gains aggregated for damage sensitivity enhancement is established, by which high-precision damage location and quantification can be realized through the proposed two-stage method. For the first step, the closed-loop strain mode shapes are used to construct the MSEBI for damage localization. For the second step, we adopt the PSO algorithm to train the parameters (weights and biases) of the neural network in order to reduce the difference between the real and expected outputs and then use the trained network for quantifying the damage extent. Furthermore, validation is completed by contemplating a two-span, bridge-like structure. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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23 pages, 6026 KiB

Open AccessArticle

Reliability Assessment Method for Simply Supported Bridge Based on Structural Health Monitoring of Frequency with Temperature and Humidity Effect Eliminated

by Xin He, Guojin Tan, Wenchao Chu, Sufeng Zhang and Xueliang Wei

Sustainability 2022, 14(15), 9600; https://doi.org/10.3390/su14159600 - 4 Aug 2022

Cited by 3 | Viewed by 1787

Abstract

Structural health monitoring (SHM) has been widely used for the performance assessment of bridges, especially the methods based on dynamic characteristics. Meanwhile, bridge modal frequency is influenced significantly by environmental factors, such as temperature and humidity. Combined with SHM, a reliability assessment of [...] Read more.

Structural health monitoring (SHM) has been widely used for the performance assessment of bridges, especially the methods based on dynamic characteristics. Meanwhile, bridge modal frequency is influenced significantly by environmental factors, such as temperature and humidity. Combined with SHM, a reliability assessment of bridges with the temperature and humidity effects eliminated is proposed. Firstly, the bridge deflection verification coefficient is adopted as the evaluation indicator for bridge condition, which is the ratio of deflection-measured value to deflection-calculated value. It is obtained from the relationship between verification coefficient and modal frequency through theoretical derivation. Secondly, a back propagation (BP) neural network is improved by using an artificial bee colony algorithm and employed as a surrogate model to eliminate the effect of temperature and humidity on frequency. Thirdly, a dynamic Bayesian network is applied to establish the reliability analysis model combined with the monitoring results, so that the probability distribution of bridge parameters is updated to improve the accuracy of the reliability analysis. Finally, a simply supported bridge is used as the case study, based on the proposed method in this work. The results indicate that the proposed method can eliminate the temperature and humidity effect on frequency precisely and effectively. With the effect of temperature and humidity on frequency eliminated, the bridge condition assessment can be evaluated accurately through the reliability analysis based on SHM and the dynamic Bayesian network. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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16 pages, 12158 KiB

Open AccessArticle

Numerical Investigation on the Dynamic Performance of Steel–Concrete Composite Continuous Rigid Bridges Subjected to Moving Vehicles

by Binqiang Guo, Renzhi Wang, Chen Lu, Weijian Shi and Qingfei Gao

Sustainability 2021, 13(24), 13666; https://doi.org/10.3390/su132413666 - 10 Dec 2021

Cited by 1 | Viewed by 2215

Abstract

Assembly construction is the main feature of industrialized bridges, and π-shaped section steel–concrete composites that are continuously rigid have been widely used in engineering fields in recent years; however, their dynamic responses and corresponding impact coefficients in positive and negative moment regions need [...] Read more.

Assembly construction is the main feature of industrialized bridges, and π-shaped section steel–concrete composites that are continuously rigid have been widely used in engineering fields in recent years; however, their dynamic responses and corresponding impact coefficients in positive and negative moment regions need to be further studied. First, considering the interface slip model, we established a finite element model for the π-shaped continuous region section of the steel–concrete composite on the Sutai Expressway Tongfu No. 3 viaduct. Second, the bridge deck unevenness parameters were generated by preparing a MATLAB program with random calculations and were added to the bridge deck as the excitation load along with the vehicle load. Such parameters are defined on the basis of considering the vertical degrees of freedom of the four wheels and of one vehicle rigid body. Finally, we analyzed the displacement or stress impact coefficients as the dynamic response index of the bridge by adjusting the vehicle travel speeds, vehicle weights, interface slip stiffness values, and deck unevenness values. The results show that the change in vehicle travel speed and the change in vehicle load weight have some influence on the change in the dynamic effect of the combined beam, but this change is not significant. Moreover, the unevenness and interface slip strength changes have a large effect on the dynamic effect of the combination beam, which can significantly change the impact coefficient of the combination beam bridge. The worse the unevenness of the bridge deck is, the lower the grade of interface slip for the steel–concrete composite bridges and the higher the impact coefficient. We calculated the recommended impact coefficient values of the steel–concrete composite bridge based on the specifications for various countries, and they range from 1.16 to 1.4; such values are similar to the finite element calculation results. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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17 pages, 6145 KiB

Open AccessArticle

Static Load Test and Numerical Analysis of Influencing Factors of the Ultimate Bearing Capacity of PHC Pipe Piles in Multilayer Soil

by Xusen Li, Jiaqiang Zhang, Hao Xu, Zhenwu Shi and Qingfei Gao

Sustainability 2021, 13(23), 13166; https://doi.org/10.3390/su132313166 - 27 Nov 2021

Cited by 3 | Viewed by 1852

Abstract

Prestressed high-strength concrete (PHC) pipe piles have been widely used in engineering fields in recent years; however, the influencing factors of their ultimate bearing capacity (UBC) in multilayer soil need to be further studied. In this paper, a static load test (SLT) and [...] Read more.

Prestressed high-strength concrete (PHC) pipe piles have been widely used in engineering fields in recent years; however, the influencing factors of their ultimate bearing capacity (UBC) in multilayer soil need to be further studied. In this paper, a static load test (SLT) and numerical analysis are performed to obtain the load transfer and key UBC factors of pipe piles. The results show that the UBC of the test pile is mainly provided by the pile shaft resistance (PSR), but the pile tip resistance (PTR) cannot be ignored. Many factors can change the UBC of pipe piles, but their effects are different. The UBC of the pipe pile is linearly related to the friction coefficient and the outer-to-inner diameter ratio. Changes in the pile length make the UBC increase sharply. Low temperatures can produce freezing stress at the pile–soil interface. The effect of changing the Young modulus of pile tip soil is relatively small. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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15 pages, 5765 KiB

Open AccessArticle

Condition Diagnosis of Long-Span Bridge Pile Foundations Based on the Spatial Correlation of High-Density Strain Measurement Points

by Feng Liu, Qianen Xu and Yang Liu

Sustainability 2021, 13(22), 12498; https://doi.org/10.3390/su132212498 - 12 Nov 2021

Cited by 4 | Viewed by 1641

Abstract

Pile foundations of long-span bridges are often deeply buried in soil, and their structural condition is difficult to accurately diagnose by conventional methods. To address this issue, a method for diagnosing the structural condition of bridge pile foundations based on the spatial correlation [...] Read more.

Pile foundations of long-span bridges are often deeply buried in soil, and their structural condition is difficult to accurately diagnose by conventional methods. To address this issue, a method for diagnosing the structural condition of bridge pile foundations based on the spatial correlation of high-density strain measurement points is proposed. The strain data of the high-density measurement points of a bridge pile foundation are obtained by using distributed optical fiber sensing technology based on Brillouin scattering, and then an algorithm for diagnosing the structural condition of the pile foundation based on geographically weighted regression analysis is presented. On this basis, aiming at the scour of the pile foundation of long-span bridges, an algorithm for estimating the scour depth of the pile foundation based on sliding plane clustering is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual bridge data. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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16 pages, 4722 KiB

Open AccessArticle

Method for Diagnosing the Uneven Settlement of a Rail Transit Tunnel Based on the Spatial Correlation of High-Density Strain Measurement Points

by Hu Li, Qianen Xu and Yang Liu

Sustainability 2021, 13(16), 9245; https://doi.org/10.3390/su13169245 - 18 Aug 2021

Cited by 5 | Viewed by 1624

Abstract

Rail transit tunnels span long distances, are large-scale structures and pass through complicated geological conditions; thus, the risk of uneven settlement cannot be ignored. To address this issue, a method for diagnosing the uneven settlement of regional railway tunnels based on the spatial [...] Read more.

Rail transit tunnels span long distances, are large-scale structures and pass through complicated geological conditions; thus, the risk of uneven settlement cannot be ignored. To address this issue, a method for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed in this study. First, with the distributed optical fiber sensing technology, a method for determining the intervals of strain measurement points with strong spatial correlations is proposed based on a support vector machine. Second, combined with the statistical analysis of the influence range of the uneven settlement of a tunnel, an algorithm for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual tunnel data. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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15 pages, 3078 KiB

Open AccessArticle

Bayesian Updates for an Extreme Value Distribution Model of Bridge Traffic Load Effect Based on SHM Data

by Xin Gao, Gengxin Duan and Chunguang Lan

Sustainability 2021, 13(15), 8631; https://doi.org/10.3390/su13158631 - 2 Aug 2021

Cited by 6 | Viewed by 2426

Abstract

As the distribution function of traffic load effect on bridge structures has always been unknown or very complicated, a probability model of extreme traffic load effect during service periods has not yet been perfectly predicted by the traditional extreme value theory. Here, we [...] Read more.

As the distribution function of traffic load effect on bridge structures has always been unknown or very complicated, a probability model of extreme traffic load effect during service periods has not yet been perfectly predicted by the traditional extreme value theory. Here, we focus on this problem and introduce a novel method based on the bridge structural health monitoring data. The method was based on the fact that the tails of the probability distribution governed the behavior of extreme values. The generalized Pareto distribution was applied to model the tail distribution of traffic load effect using the peak-over-threshold method, while the filtered Poisson process was used to model the traffic load effect stochastic process. The parameters of the extreme value distribution of traffic load effect during a service period could be determined by theoretical derivation if the parameters of tail distribution were estimated. Moreover, Bayes’ theorem was applied to update the distribution model to reduce the statistical uncertainty. Finally, the rationality of the proposed method was applied to analyze the monitoring data of concrete-filled steel tube arch bridge suspenders. The results proved that the approach was convenient and found that the extreme value distribution type III might be more suitable as the traffic load effect probability model. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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Review

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25 pages, 4822 KiB

Open AccessReview

A Review of Vibration-Based Scour Diagnosis Methods for Bridge Foundation

by Zhenhao Zhang, Guowei Lin, Xiaopeng Yang, Shilin Cui, Yan Li, Xueqing Shi and Zhongyu Han

Sustainability 2023, 15(10), 8210; https://doi.org/10.3390/su15108210 - 18 May 2023

Cited by 1 | Viewed by 1524

Abstract

Foundation scour poses a serious threat to bridge safety in the whole life cycle and leads to many bridge failure incidents. Recently, as an important subfield of bridge structural health monitoring, vibration-based scour diagnosis methods have garnered widespread attention, particularly due to their [...] Read more.

Foundation scour poses a serious threat to bridge safety in the whole life cycle and leads to many bridge failure incidents. Recently, as an important subfield of bridge structural health monitoring, vibration-based scour diagnosis methods have garnered widespread attention, particularly due to their rapid and low-cost features, which overcomes the difficulties of complex equipment installation associated with the traditional approaches. Recent advances of this method within the last decade are reviewed in this paper. Firstly, the principle of scour diagnosis and vibration excitation methods are introduced. Then, existing qualitative and quantitative studies on scour diagnosis are reviewed, respectively. The former refers to identifying the scour location based on the bridge dynamic characteristics or dynamic response changes, and the latter refers to identifying scour depth based on model updating or machine learning methods. Based on the above review, some important but neglected issues are summarized and discussed in depth, and some challenges and future trends are proposed, including innovative excitation methods, mitigation of environmental conditions interference, soil–structure interaction prediction and application of machine learning techniques. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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18 pages, 2656 KiB

Open AccessReview

State-of-the-Art Review of the Resilience of Urban Bridge Networks

by Tong Wang, Yang Liu, Qiyuan Li, Peng Du, Xiaogong Zheng and Qingfei Gao

Sustainability 2023, 15(2), 989; https://doi.org/10.3390/su15020989 - 5 Jan 2023

Cited by 3 | Viewed by 2118

Abstract

With the rapid advancement of the urbanization process, the bridge networks in cities are becoming increasingly optimized, playing an important role in ensuring the normal operation of cities. However, with the gradual deterioration of bridges and the further attenuation of their capacity, many [...] Read more.

With the rapid advancement of the urbanization process, the bridge networks in cities are becoming increasingly optimized, playing an important role in ensuring the normal operation of cities. However, with the gradual deterioration of bridges and the further attenuation of their capacity, many bridges are prone to damage or even collapse under extreme loads. After a natural disaster or human-derived accident occurs in a city, the normal operation of the bridge network in the city will play an irreplaceable role in emergency rescue and long-term recovery after the disaster. In this paper, the resilience of urban bridge networks, as a comprehensive indicator that integrates predisaster early warning, disaster response and postdisaster recovery information, is considered. This indicator has been applied in many disciplines, such as civil engineering, sociology, management and economics. The concept of resilience is expounded, and functional and resilience assessment indicators for bridge networks are established. Additionally, the research progress on bridge network resilience is described. Finally, combined with research hotspots such as big data, artificial intelligence and bridge structural health monitoring, the development trends and prospects of bridge network resilience research are discussed. Full article

(This article belongs to the Special Issue Structural Health Monitoring in Civil Infrastructure)

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