2022

Jump to: 2021, 2020

4 pages, 604 KiB

Open AccessEditorial

Artificial-Intelligence-Based Methods for Structural Health Monitoring

by Wael A. Altabey and Mohammad Noori

Appl. Sci. 2022, 12(24), 12726; https://doi.org/10.3390/app122412726 - 12 Dec 2022

Cited by 11 | Viewed by 2180

Intelligent and resilient infrastructure and smart cities make up a rapidly emerging field that is redefining the future of urban development and ways of preserving the existing infrastructure against natural hazards... Full article

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21 pages, 4574 KiB

Open AccessArticle

Structural Assessment under Uncertain Parameters via the Interval Optimization Method Using the Slime Mold Algorithm

by Ramin Ghiasi, Mohammad Noori, Sin-Chi Kuok, Ahmed Silik, Tianyu Wang, Francesc Pozo and Wael A. Altabey

Appl. Sci. 2022, 12(4), 1876; https://doi.org/10.3390/app12041876 - 11 Feb 2022

Cited by 19 | Viewed by 2346

Abstract

Damage detection of civil and mechanical structures based on measured modal parameters using model updating schemes has received increasing attention in recent years. In this study, for uncertainty-oriented damage identification, a non-probabilistic structural damage identification (NSDI) technique based on an optimization algorithm and [...] Read more.

Damage detection of civil and mechanical structures based on measured modal parameters using model updating schemes has received increasing attention in recent years. In this study, for uncertainty-oriented damage identification, a non-probabilistic structural damage identification (NSDI) technique based on an optimization algorithm and interval mathematics is proposed. In order to take into account the uncertainty quantification, the elastic modulus is described as unknown-but-bounded interval values and the proposed new scheme determines the upper and lower bounds of the damage index. In this method, the interval bounds can provide supports for structural health diagnosis under uncertain conditions by considering the uncertainties in the variables of optimization algorithm. The model updating scheme is subsequently used to predict the interval-bound of the Elemental Stiffness Parameter (ESP). The slime mold algorithm (SMA) is used as the main algorithm for model updating. In addition, in this study, an enhanced variant of SMA (ESMA) is developed, which removes unchanged variables after a defined number of iterations. The method is implemented on three well-known numerical examples in the domain of structural health monitoring under single damage and multi-damage scenarios with different degrees of uncertainty. The results show that the proposed NSDI methodology has reduced computation time, by at least 30%, in comparison with the probabilistic methods. Furthermore, ESMA has the capability to detect damaged elements with higher certainty and lower computation cost in comparison with the original SMA. Full article

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2021

Jump to: 2022, 2020

18 pages, 8154 KiB

Open AccessArticle

Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

by Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa, Alexandre Abrahão Cury and Roberto Leal Pimentel

Appl. Sci. 2021, 11(24), 11965; https://doi.org/10.3390/app112411965 - 16 Dec 2021

Cited by 9 | Viewed by 2043

Abstract

The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring (SHM) field, [...] Read more.

The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring (SHM) field, especially when dealing with real-case structures. The main idea is to use the SAE to extract relevant features from the monitored signals and the well-known Support Vector Machine (SVM) to classify such characteristics within the context of an SHM problem. Vibration data from a numerical beam model and a highway viaduct in Brazil are considered to assess the proposed approach. In both analyzed examples, the efficiency of the implemented methodology achieved more than 99% of correct damage structural classifications, supporting the conclusion that SAE can extract relevant characteristics from dynamic signals that are useful for SHM applications. Full article

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18 pages, 4485 KiB

Open AccessArticle

Bridge Damping Extraction Method from Vehicle–Bridge Interaction System Using Double-Beam Model

by Fengzong Gong, Fei Han, Yingjie Wang and Ye Xia

Appl. Sci. 2021, 11(21), 10304; https://doi.org/10.3390/app112110304 - 02 Nov 2021

Cited by 6 | Viewed by 2249

Abstract

When vehicles interact with a bridge, a vehicle–bridge interaction (VBI) system is created. The frequency and modal shape of VBI systems have been widely studied, but the damping of VBI systems has not been adequately investigated. In recent years, several incidents of abnormal [...] Read more.

When vehicles interact with a bridge, a vehicle–bridge interaction (VBI) system is created. The frequency and modal shape of VBI systems have been widely studied, but the damping of VBI systems has not been adequately investigated. In recent years, several incidents of abnormal bridge vibration due to changes in bridge damping have occurred and aroused widespread concern in society. Damping is an important evaluation index of structural dynamic performance. Knowing the damping ratio of a VBI system is useful for analyzing the damping changes while a bridge is in service. This paper presents a method to extract bridge damping values from a VBI system, which can serve as a guide for bridge damping evaluation. First, a double-beam theoretical model was used to simplify the VBI system for cases involving uniform traffic flow. The damping ratio equation for the simplified VBI system was obtained using the extended dynamic stiffness method (EDSM). A double-beam finite element model and a VBI finite element model were established. The damping ratios of the two models were separately calculated and then compared with the simplified VBI model results. The results verified the accuracy of the simplified method. This paper then explains that bridge damping values can be extracted by estimating the equivalent traffic flow parameters and using the damping formula for the simplified VBI system. The bridge damping ratios extracted using this method in an engineering case ranged from 0.75% to 0.78%, which is smaller than the range that was directly identified using monitoring data (0.83–1.19%). The results show that the method can effectively extract bridge damping ratios and improve damping ratio identification. Full article

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19 pages, 2683 KiB

Open AccessArticle

Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity

by Qingxia Zhang, Dengzheng Xu, Jilin Hou, Łukasz Jankowski and Haiyan Wang

Appl. Sci. 2021, 11(21), 10152; https://doi.org/10.3390/app112110152 - 29 Oct 2021

Cited by 3 | Viewed by 1154

Abstract

Damage identification methods based on structural modal parameters are influenced by the structure form, number of measuring sensors and noise, resulting in insufficient modal data and low damage identification accuracy. The additional virtual mass method introduced in this study is based on the [...] Read more.

Damage identification methods based on structural modal parameters are influenced by the structure form, number of measuring sensors and noise, resulting in insufficient modal data and low damage identification accuracy. The additional virtual mass method introduced in this study is based on the virtual deformation method for deriving the frequency-domain response equation of the virtual structure and identify its mode to expand the modal information of the original structure. Based on the initial condition assumption that the structural damage was sparse, the damage identification method based on sparsity with

l_{1}

and

l_{2}

norm of the damage-factor variation and the orthogonal matching pursuit (OMP) method based on the

l_{0}

norm were introduced. According to the characteristics of the additional virtual mass method, an improved OMP method (IOMP) was developed to improve the localization of optimal solution determined using the OMP method and the damage substructure selection process, analyze the damage in the entire structure globally, and improve damage identification accuracy. The accuracy and robustness of each damage identification method for multi-damage scenario were analyzed and verified through simulation and experiment. Full article

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15 pages, 4110 KiB

Open AccessArticle

Structural Damage Identification Using a Modified Directional Bat Algorithm

by Yonghui Su, Lijun Liu and Ying Lei

Appl. Sci. 2021, 11(14), 6507; https://doi.org/10.3390/app11146507 - 15 Jul 2021

Cited by 11 | Viewed by 1660

Abstract

Bat algorithm (BA) has been widely used to solve optimization problems in different fields. However, there are still some shortcomings of standard BA, such as premature convergence and lack of diversity. To solve this problem, a modified directional bat algorithm (MDBA) is proposed [...] Read more.

Bat algorithm (BA) has been widely used to solve optimization problems in different fields. However, there are still some shortcomings of standard BA, such as premature convergence and lack of diversity. To solve this problem, a modified directional bat algorithm (MDBA) is proposed in this paper. Based on the directional bat algorithm (DBA), the individual optimal updating mechanism is employed to update a bat’s position by using its own optimal solution. Then, an elimination strategy is introduced to increase the diversity of the population, in which individuals with poor fitness values are eliminated, and new individuals are randomly generated. The proposed algorithm is applied to the structural damage identification and to an objective function composed of the actual modal information and the calculated modal information. Finally, the proposed MDBA is used to solve the damage detection of a beam-type bridge and a truss-type bridge, and the results are compared with those of other swarm intelligence algorithms and other variants of BA. The results show that in the case of the same small population number and few iterations, MDBA has more accurate identification and better convergence than other algorithms. Moreover, the study on anti-noise performance of the MDBA shows that the maximum relative error is only 5.64% at 5% noise level in the beam-type bridge, and 6.53% at 3% noise in the truss-type bridge, which shows good robustness. Full article

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27 pages, 11828 KiB

Open AccessArticle

Auto-Regressive Integrated Moving-Average Machine Learning for Damage Identification of Steel Frames

by Yuqing Gao, Khalid M. Mosalam, Yueshi Chen, Wei Wang and Yiyi Chen

Appl. Sci. 2021, 11(13), 6084; https://doi.org/10.3390/app11136084 - 30 Jun 2021

Cited by 16 | Viewed by 1998

Abstract

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based structural health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends in machine learning (ML) technologies, automated structural damage [...] Read more.

Auto-regressive (AR) time series (TS) models are useful for structural damage detection in vibration-based structural health monitoring (SHM). However, certain limitations, e.g., non-stationarity and subjective feature selection, have reduced its wide-spread use. With increasing trends in machine learning (ML) technologies, automated structural damage recognition is becoming popular and attracting many researchers. In this paper, we combined TS modeling and ML classification to automatically extract damage features and overcome the limitation of non-stationarity. We propose a two-stage framework, namely auto-regressive integrated moving-average machine learning (ARIMA-ML) with modules for pre-processing, model parameter determination, feature extraction, and classification. Based on shaking table tests of a space steel frame, floor acceleration data were collected and labeled according to experimental observations and records. Subsequently, we designed three damage classification tasks for: (1) global damage detection, (2) local damage detection, and (3) local damage pattern recognition. The results from these three tasks indicated the robustness and accuracy of the proposed framework where 97%, 98%, and 80% average segment accuracy were achieved, respectively. The confusion matrix results showed the unbiased model performance even under an imbalanced-class distribution. In summary, the presented study revealed the high potential of the proposed ARIMA-ML framework in vibration-based SHM. Full article

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20 pages, 6272 KiB

Open AccessArticle

A Novel Decision Support System for Long-Term Management of Bridge Networks

by Enes Karaaslan, Ulas Bagci and Necati Catbas

Appl. Sci. 2021, 11(13), 5928; https://doi.org/10.3390/app11135928 - 25 Jun 2021

Cited by 7 | Viewed by 2016

Abstract

Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in [...] Read more.

Developing a bridge management strategy at the network level with efficient use of capital is very important for optimal infrastructure remediation. This paper introduces a novel decision support system that considers many aspects of bridge management and successfully implements the investigated methodology in a web-based platform. The proposed decision support system uses advanced prediction models, decision trees, and incremental machine learning algorithms to generate an optimal decision strategy. The system aims to achieve adaptive and flexible decision making while entailing powerful utilization of nondestructive evaluation (NDE) methods. The NDE data integration and visualization allow automatic retrieval of inspection results and overlaying the defects on a 3D bridge model. Furthermore, a deep learning-based damage growth prediction model estimates the future condition of the bridge elements and utilizes this information in the decision-making process. The decision ranking takes into account a wide range of factors including structural safety, serviceability, rehabilitation cost, life cycle cost, and societal and political factors to generate optimal maintenance strategies with multiple decision alternatives. This study aims to bring a complementary solution to currently in-use systems with the utilization of advanced machine-learning models and NDE data integration while still equipped with main bridge management functions of bridge management systems and capable of transferring data to other systems. Full article

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18 pages, 2139 KiB

Open AccessFeature PaperArticle

Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

by Sifat Muin and Khalid M. Mosalam

Appl. Sci. 2021, 11(12), 5727; https://doi.org/10.3390/app11125727 - 21 Jun 2021

Cited by 16 | Viewed by 4773

Abstract

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative [...] Read more.

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN₁₀ and ANN₁₀₀), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the

C A V

and the relative

C A V

with respect to the linear response, i.e.,

R_{C A V}

, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with

C A V

and

R_{C A V}

as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency. Full article

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22 pages, 3048 KiB

Open AccessArticle

Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning

by Shimin Tang and Zhiqiang Chen

Appl. Sci. 2021, 11(9), 3952; https://doi.org/10.3390/app11093952 - 27 Apr 2021

Cited by 6 | Viewed by 2259

Abstract

With the ubiquitous use of mobile imaging devices, the collection of perishable disaster-scene data has become unprecedentedly easy. However, computing methods are unable to understand these images with significant complexity and uncertainties. In this paper, the authors investigate the problem of disaster-scene understanding [...] Read more.

With the ubiquitous use of mobile imaging devices, the collection of perishable disaster-scene data has become unprecedentedly easy. However, computing methods are unable to understand these images with significant complexity and uncertainties. In this paper, the authors investigate the problem of disaster-scene understanding through a deep-learning approach. Two attributes of images are concerned, including hazard types and damage levels. Three deep-learning models are trained, and their performance is assessed. Specifically, the best model for hazard-type prediction has an overall accuracy (OA) of 90.1%, and the best damage-level classification model has an explainable OA of 62.6%, upon which both models adopt the Faster R-CNN architecture with a ResNet50 network as a feature extractor. It is concluded that hazard types are more identifiable than damage levels in disaster-scene images. Insights are revealed, including that damage-level recognition suffers more from inter- and intra-class variations, and the treatment of hazard-agnostic damage leveling further contributes to the underlying uncertainties. Full article

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28 pages, 5752 KiB

Open AccessArticle

Evaluation of the Mechanical Properties of Normal Concrete Containing Nano-MgO under Freeze–Thaw Conditions by Evolutionary Intelligence

by Mehdi Yazdchi, Ali Foroughi Asl, Siamak Talatahari and Amir H. Gandomi

Appl. Sci. 2021, 11(6), 2529; https://doi.org/10.3390/app11062529 - 12 Mar 2021

Cited by 6 | Viewed by 1620

Abstract

In this research, different amounts of nano-MgO were added to normal concrete samples, and the effect of these particles on the durability of the samples under freeze and thaw conditions was investigated. The compressive and tensile strength as well as the permeability of [...] Read more.

In this research, different amounts of nano-MgO were added to normal concrete samples, and the effect of these particles on the durability of the samples under freeze and thaw conditions was investigated. The compressive and tensile strength as well as the permeability of concrete containing nanoparticles were measured and compared to those of plain samples (without nanoparticles). The age of concrete samples, percentage of nanoparticles, and water-to-binder ratio are the variables of the current research. Based on the results, the addition of 1% nano-MgO to the normal concrete with a water-to-binder ratio of 0.44 can reduce the permeability up to 63% and improve the compressive and tensile strengths by 9.12% and 10.6%, respectively. Gene Expression Programming (GEP) is applied, and three formulations are derived for the prediction of mechanical properties of concrete containing nano-MgO. In this method, 80% of the dataset is used randomly for the training process and 20% is utilized for testing the formulation. The results obtained by GEP showed acceptable accuracy. Full article

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17 pages, 29315 KiB

Open AccessArticle

Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables

by Shitong Hou, Bin Dong, Jianhua Fan, Gang Wu, Haochen Wang, Yitian Han and Xiaojin Zhao

Appl. Sci. 2021, 11(3), 1254; https://doi.org/10.3390/app11031254 - 29 Jan 2021

Cited by 17 | Viewed by 2039

Abstract

Stay cables are important structural members of cable-stayed bridges, which play a significant role in the health monitoring and assessment of cable-stayed bridges. The in-service cable force, which varies from the effects of vehicle load, wind load and other environmental factors, may cause [...] Read more.

Stay cables are important structural members of cable-stayed bridges, which play a significant role in the health monitoring and assessment of cable-stayed bridges. The in-service cable force, which varies from the effects of vehicle load, wind load and other environmental factors, may cause fatigue damage in stay cables. Traditional force identification methods can only calculate the time-average cable force instead of the instantaneous force. A novel method has been proposed in this paper for identifying time-varying cable tension based on the variational mode decomposition (VMD) method. This recent method decomposes signals and adaptively estimates instantaneous frequency combined with the Hilbert–Huang transform method. In the proposed study, the time-varying modal frequencies were identified from stay cable acceleration data, and then the time-varying cable tension was identified by the relationship between cable tension and identified fundamental frequency. Scaled and full-scale models of stay cables were implemented successively to illustrate the validity of the proposed method. The results showed that the variational mode decomposition (VMD) method has a good effect on identifying the time-varying cable forces, even the sudden changes in cable force. According to the cable force identification results, the maximum error was 8.4%, which meets the actual application of time-varying cable force measurements. An on-site test was also implemented to monitor the cable force during a construction period, and the results showed that the proposed method can provide accurate real-time results for evaluation and decision-making. Full article

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17 pages, 14551 KiB

Open AccessArticle

Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach

by Ramin Ghiasi, Mohammad Noori, Wael A. Altabey, Ahmed Silik, Tianyu Wang and Zhishen Wu

Appl. Sci. 2021, 11(2), 770; https://doi.org/10.3390/app11020770 - 15 Jan 2021

Cited by 34 | Viewed by 2807

Abstract

Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to [...] Read more.

Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort. Full article

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16 pages, 5068 KiB

Open AccessArticle

InstaDam: Open-Source Platform for Rapid Semantic Segmentation of Structural Damage

by Vedhus Hoskere, Fouad Amer, Doug Friedel, Wanxian Yang, Yu Tang, Yasutaka Narazaki, Matthew D. Smith, Mani Golparvar-Fard and Billie F. Spencer, Jr.

Appl. Sci. 2021, 11(2), 520; https://doi.org/10.3390/app11020520 - 07 Jan 2021

Cited by 7 | Viewed by 3278

Abstract

The tremendous success of automated methods for the detection of damage in images of civil infrastructure has been fueled by exponential advances in deep learning over the past decade. In particular, many efforts have taken place in academia and more recently in industry [...] Read more.

The tremendous success of automated methods for the detection of damage in images of civil infrastructure has been fueled by exponential advances in deep learning over the past decade. In particular, many efforts have taken place in academia and more recently in industry that demonstrate the success of supervised deep learning methods for semantic segmentation of damage (i.e., the pixel-wise identification of damage in images). However, in graduating from the detection of damage to applications such as inspection automation, efforts have been limited by the lack of large open datasets of real-world images with annotations for multiple types of damage, and other related information such as material and component types. Such datasets for structural inspections are difficult to develop because annotating the complex and amorphous shapes taken by damage patterns remains a tedious task (requiring too many clicks and careful selection of points), even with state-of-the art annotation software. In this work, InstaDam—an open source software platform for fast pixel-wise annotation of damage—is presented. By utilizing binary masks to aid user input, InstaDam greatly speeds up the annotation process and improves the consistency of annotations. The masks are generated by applying established image processing techniques (IPTs) to the images being annotated. Several different tunable IPTs are implemented to allow for rapid annotation of a wide variety of damage types. The paper first describes details of InstaDam’s software architecture and presents some of its key features. Then, the benefits of InstaDam are explored by comparing it to the Image Labeler app in Matlab. Experiments are conducted where two employed student annotators are given the task of annotating damage in a small dataset of images using Matlab, InstaDam without IPTs, and InstaDam. Comparisons are made, quantifying the improvements in annotation speed and annotation consistency across annotators. A description of the statistics of the different IPTs used for different annotated classes is presented. The gains in annotation consistency and efficiency from using InstaDam will facilitate the development of datasets that can help to advance research into automation of visual inspections. Full article

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17 pages, 13794 KiB

Open AccessArticle

Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet

by Jiyuan Shi, Ji Dang, Mida Cui, Rongzhi Zuo, Kazuhiro Shimizu, Akira Tsunoda and Yasuhiro Suzuki

Appl. Sci. 2021, 11(2), 518; https://doi.org/10.3390/app11020518 - 07 Jan 2021

Cited by 36 | Viewed by 4263

Abstract

In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct [...] Read more.

In this research, 200 corrosion images of steel and 500 crack images of rubber bearing are collected and manually labeled to build the data set. Then the two data sets are respectively adopted to train VGG-Unet models in two methods, aiming to conduct Damage Segmentation by inputting different size of data set. One method is Squashing Segmentation to input squashed images from high resolution directly into VGG-Unet model while Cropping Segmentation uses cropped image with size 224 × 224 as input images. Because the proportion of damage pixels in the data set is different, the results produced by the two data sets are quite different. For large size damage (such as corrosion) segmentation, Cropping Segmentation has a better result while for minor damage (such as crack) segmentation, the result is opposite. The main reason is the gap in the concentration of valid data from the data set. To improve the capability of crack segmentation based on Cropping Segmentation, Background Data Drop Rate (BDDR) is adopted to reduce the quantity of background images to control the proportion of damage pixels from the data set in pixel-level. The ratio of damage pixels from the data set can be decided by different value of BDDR. By testing, the accuracy of Cropping Segmentation becomes relatively higher under BDDR being 0.8. Full article

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2020

Jump to: 2022, 2021

16 pages, 4145 KiB

Open AccessArticle

System Reliability Assessment of Cable-Supported Bridges under Stochastic Traffic Loads Based on Deep Belief Networks

by Naiwei Lu, Yang Liu, Mohammad Noori and Xinhui Xiao

Appl. Sci. 2020, 10(22), 8049; https://doi.org/10.3390/app10228049 - 13 Nov 2020

Cited by 13 | Viewed by 1967

Abstract

A cable-supported bridge is usually a key junction of a highway or a railway that demands a higher safety margin, especially when it is subjected to harsh environmental and complex loading conditions. In comparison to short-span girder bridges, long-span flexible structures have unique [...] Read more.

A cable-supported bridge is usually a key junction of a highway or a railway that demands a higher safety margin, especially when it is subjected to harsh environmental and complex loading conditions. In comparison to short-span girder bridges, long-span flexible structures have unique characteristics that increase the complexity of the structural mechanical behavior. Therefore, the system safety of cable-supported bridges is critical but difficult to evaluate. This study proposes a novel and intelligent approach for system reliability evaluation of cable-supported bridges under stochastic traffic load by utilizing deep belief networks (DBNs). The related mathematical models were derived taking into consideration the structural nonlinearities and high-order statically indeterminate characteristics. A computational framework is presented to illustrate the steps followed for system reliability evaluation using DBNs. In a case study, a prototype suspension bridge is selected to investigate the system reliability under stochastic traffic loading based on site-specific traffic monitoring data. The numerical results indicated that DBNs provide an accurate approximation for the mechanical behavior accounting for structural nonlinearities and different system behaviors, which can be treated as a meta-model to estimate the structural failure probability. The dominant failure modes of the suspension bridge are the fracture of suspenders followed by the bending failure of girders. The degradation of suspenders due to fatigue-corrosion damage has a significant effect on the system reliability of a suspension bridge. The numerical results provide a theoretical basis for the design on cable replacement strategies. Full article

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