Open Access

ARTICLE

Rapid and Accurate Identification of Concrete Surface Cracks via a Lightweight & Efficient YOLOv3 Algorithm

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 363-380, 2024, DOI:10.32604/sdhm.2024.042388
Abstract Concrete materials and structures are extensively used in transformation infrastructure and they usually bear cracks during their long-term operation. Detecting cracks using deep-learning algorithms like YOLOv3 (You Only Look Once version 3) is a new trend to pursue intelligent detection of concrete surface cracks. YOLOv3 is a typical deep-learning algorithm used for object detection. Owing to its generality, YOLOv3 lacks specific efficiency and accuracy in identifying concrete surface cracks. An improved algorithm based on YOLOv3, specialized in the rapid and accurate identification of concrete surface cracks is worthy of investigation. This study proposes a tailored… More >

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ARTICLE

An Innovative Technique to Measure Lateral Pressure of Self-Compacting Concrete Using Fiber Bragg Grating Sensor

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 395-408, 2024, DOI:10.32604/sdhm.2024.049366
Abstract Self-compacting concrete (SCC) is the most flowable concrete type that exerts high pressure on formwork. SCC is the most commonly used concrete globally for construction applications due to its cost-effectiveness. However, to make a formwork resist the exerted lateral pressure of SCC, it is required to have a suitable design for formwork. This paper presents a novel approach on how could create and prepare the Fiber Bragg Grating (FBG) optics using as a sensor to measure lateral pressure and temperature of SCC. To ensure the FBG sensor works properly a validated methodology is conducted. In More >

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ARTICLE

Reinforcement Effect of Recycled CFRP on Cement-Based Composites: With a Comparison to Commercial Carbon Fiber Powder

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 409-423, 2024, DOI:10.32604/sdhm.2024.048597
Abstract In this paper, recycled carbon fiber reinforced polymer (CFRP) mixture (CFRP-M, including recycled carbon fiber and powder) and refined recycled CFRP fiber (CFRP-F, mostly recycled carbon fiber) were added to cement to study the influence of addition on the flexural strength, compressive strength, and fluidity of cement-based materials. The recycled CFRP were prepared by mechanically processing the prepreg scraps generated during the manufacture of CFRP products. For comparison, commercial carbon fiber powder was also added in cement and the performance was compared to that of addition of recycled CFRP. The hydration products and strengthening mechanism… More >

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ARTICLE

Enhanced Transmission Tower Foundation Reliability Assessment: A Fuzzy Comprehensive Evaluation Framework

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 425-444, 2024, DOI:10.32604/sdhm.2024.046584
（This article belongs to the Special Issue: Health Monitoring and Rapid Evaluation of Infrastructures)
Abstract Due to the lack of a quantitative basis for the inspection, evaluation, and identification of existing transmission tower foundations, a new fuzzy comprehensive evaluation method is proposed to assess the reliability of transmission tower foundation bearing capacity. This method is based on the reliability analysis of the transmission tower foundation bearing capacity by analyzing the sensitivity of degradation of detection indexes on the reliability of transmission tower foundation bearing capacity, the weighting coefficient matrix is established about the influencing factors in the evaluation model. Through the correlation analysis between the bearing capacity degradation of the More >

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ARTICLE

Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298
（This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
Abstract The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is More >

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ARTICLE

Identification of Damage in Steel‒Concrete Composite Beams Based on Wavelet Analysis and Deep Learning

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 465-483, 2024, DOI:10.32604/sdhm.2024.048705
（This article belongs to the Special Issue: Sensing Data Based Structural Health Monitoring in Engineering)
Abstract In this paper, an intelligent damage detection approach is proposed for steel-concrete composite beams based on deep learning and wavelet analysis. To demonstrate the feasibility of this approach, first, following the guidelines provided by relevant standards, steel-concrete composite beams are designed, and six different damage incidents are established. Second, a steel ball is used for free-fall excitation on the surface of the steel-concrete composite beams and a low-temperature-sensitive quasi-distributed long-gauge fiber Bragg grating (FBG) strain sensor is used to obtain the strain signals of the steel-concrete composite beams with different damage types. To reduce the… More >

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ARTICLE

Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698
Abstract This study addresses the issue of inaccurate single damage fingerprint recognition during the process of bridge damage identification. To improve accuracy, the proposed approach involves fusing displacement mode difference and curvature mode difference data for single damage identification, and curvature mode difference and displacement mode wavelet coefficient difference data for two damage identification. The methodology begins by establishing a finite element model of the cable-stayed bridge and obtaining the original damage fingerprints, displacement modes, curvature modes, and wavelet coefficient differences of displacement modes through modal analysis. A fusion program based on the D-S evidence theory… More >

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ARTICLE

Effects of Incorporating Steel Fibers and Municipal Waste on the Compressive Strength of Concrete

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 505-524, 2024, DOI:10.32604/sdhm.2024.049363
Abstract In this study, we assessed the impact of substituting natural fine aggregates with municipal solid waste incineration bottom ash (MSWI-BA) in steel fiber (SF)-reinforced concrete on its compressive properties post high-temperature exposure. The concrete specimens incorporating MSWI-BA as the fine aggregate and SFs for reinforcement underwent uniaxial compression tests after exposure to high temperatures. Through the tests, we investigated the impact of high-temperature exposure on mechanical properties, such as mass loss rate, stress-strain full curve, compressive strength, peak strain, elastic modulus, and so on, over different thermostatic durations. The analysis revealed that with the increasing… More >

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ARTICLE

Health Monitoring-Based Assessment of Reinforcement with Prestressed Steel Strand for Cable-Stayed Bridge

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 53-80, 2022, DOI:10.32604/sdhm.2021.016130
（This article belongs to the Special Issue: Vortex-induced vibration of long-span bridges)
Abstract This paper presents the method of reinforcing main girder of reinforced concrete cable-stayed bridge with prestressed steel strands. To verify the effectiveness of external prestressed strand reinforcement method. Static load tests and health monitoring-based assessment were carried out before and after reinforcement. Field load test shows that the deflection and stress values of the main girder are reduced by 10%~20% after reinforcement, and the flexural strength and stiffness of the strengthened beam are improved. The deflection and strain data of health monitoring of the specified section are collected. The deflection of the second span is More >

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ARTICLE

The Coupled Effect of Temperature Changes and Damage Depth on Natural Frequencies in Beam-Like Structures

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 15-35, 2022, DOI:10.32604/sdhm.2022.020418
Abstract A significant amount of research is concerned with dynamic modal parameters for damage detection of structural conditions due to their simplicity in use and feasibility. However, their use for damage detection should be performed with special attention, particularly in operational and environmental conditions subjected to temperature changes. Beams in construction industries experience different loading types, such as temperature changes leading to crack initiation and propagation. Changed physical and dynamic properties such as natural frequencies and mode shapes indicate that damage has occurred within the structures. In this study, vibration analysis of cantilever and cantilever simply… More >

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1570

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ARTICLE

Shape Sensing of Thin Shell Structure Based on Inverse Finite Element Method

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 1-14, 2022, DOI:10.32604/sdhm.2022.019554
Abstract Shape sensing as a crucial component of structural health monitoring plays a vital role in real-time actuation and control of smart structures, and monitoring of structural integrity. As a model-based method, the inverse finite element method (iFEM) has been proved to be a valuable shape sensing tool that is suitable for complex structures. In this paper, we propose a novel approach for the shape sensing of thin shell structures with iFEM. Considering the structural form and stress characteristics of thin-walled structure, the error function consists of membrane and bending section strains only which is consistent… More >

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1507

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ARTICLE

Dynamic Risk-Warning of Center Diaphragm and Bench Composite Method During Construction

Structural Durability & Health Monitoring, Vol.16, No.3, pp. 235-254, 2022, DOI:10.32604/sdhm.2022.013141
Abstract During the construction of subway tunnels, safety issues should not be ignored, so it is necessary to prevent and resolve the risk in time and accurately. However, there are some shortcomings in the research of risk assessment, such as the subjectivity of initial data or the lack of scientific evaluation model, in order to solve the problem, this paper relies on the Changping section of the Guanhui Intercity Metro, in order to establish a dynamic risk-warning model for the construction process of subway tunnel with the CD-Bench composite method. First, a monitoring plan was equationted… More >

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ARTICLE

Reconstruction Technology of Flexible Structure Shape Based on FBG Sensor Array and Deep Learning Algorithm

Structural Durability & Health Monitoring, Vol.16, No.2, pp. 179-194, 2022, DOI: 10.32604/sdhm.2022.018202
Abstract A structural displacement field reconstruction method is proposed to aim at the problems of deformation monitoring and displacement field reconstruction of flexible plate-like structures in the aerospace field. This method combines the deep neural network model of the cross-layer connection structure with the fiber grating sensor network. This paper first introduces the principle of strain detection of fiber grating sensor, studies the mapping relationship between strain and displacement, and proposes a strain-displacement conversion model based on an improved neural network. Then the intelligent structure deformation monitoring system is built. By controlling the stepping distance of… More >

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2016

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1089

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ARTICLE

Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 81-96, 2022, DOI:10.32604/sdhm.2022.016905
Abstract Fatigue crack prediction is a critical aspect of prognostics and health management research. The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth, and it is widely used to predict the state of fatigue crack. This paper proposes a method of lamb wave-based early fatigue microcrack prediction with the aid of particle filters. With this method, which the changes in signal characteristics under different fatigue crack lengths are analyzed, and the state- and observation-equations of crack extension are established. Furthermore, an experiment is More >

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2001

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1384

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REVIEW

Review Article on Condition Assessment of Structures Using Electro-Mechanical Impedance Technique

Structural Durability & Health Monitoring, Vol.16, No.2, pp. 97-128, 2022, DOI:10.32604/sdhm.2022.015732
Abstract Structural health monitoring (SHM) is a process for determination of presence, location, severity of damages and remaining life of the infrastructures. SHM is widely applied in aerospace, mechanical and civil engineering systems to assess the conditions of structures to improve the operation, safety, serviceability and reliability, respectively. There are various SHM techniques for monitoring the health of structures such as global response based and local techniques. Damages occur in the structures due to its inability to withstand intended design loadings, physical environment and chemical environment. Therefore, damage identification is necessary to improve the durability of… More >

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1992

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1016

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ARTICLE

PSO-LSSVM-based Online SOC Estimation for Simulation Substation Battery

Structural Durability & Health Monitoring, Vol.16, No.1, pp. 37-51, 2022, DOI:10.32604/sdhm.2022.018422
Abstract As the emergency power supply for a simulation substation, lead-acid batteries have a work pattern featuring non-continuous operation, which leads to capacity regeneration. However, the accurate estimation of battery state of charge (SOC), a measurement of the amount of energy available in a battery, remains a hard nut to crack because of the non-stationarity and randomness of battery capacity change. This paper has proposed a comprehensive method for lead-acid battery SOC estimation, which may aid in maintaining a reasonable charging schedule in a simulation substation and improving battery’s durability. Based on the battery work pattern, More >

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1290

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ARTICLE

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Structural Durability & Health Monitoring, Vol.16, No.3, pp. 271-290, 2022, DOI:10.32604/sdhm.2022.010622
Abstract The regulation of tyre pressure is treated as a significant aspect of ‘tyre maintenance’ in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the… More >

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ARTICLE

Experimental Study on the Degradation of Bonding Behavior between Reinforcing Bars and Concrete after Corrosion and Fatigue Damage

Structural Durability & Health Monitoring, Vol.16, No.3, pp. 195-212, 2022, DOI:10.32604/sdhm.2022.08886
Abstract In marine environments, the durability of reinforced concrete structures such as bridges, which suffer from the coupled effects of corrosion and fatigue damage, is significantly reduced. Fatigue loading can result in severe deterioration of the bonds between reinforcing steel bars and the surrounding concrete, particularly when reinforcing bars are corroded. Uniaxial tension testing was conducted under static loading and fatigue loading conditions to investigate the bonding characteristics between corroded reinforcing bars and concrete. An electrolyte corrosion technique was used to accelerate steel corrosion. The results show that the bond strength was reduced under fatigue loading, More >

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913

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ARTICLE

Optimal Sensor Placement for Structural, Damage and Impact Identification: A Review

Structural Durability & Health Monitoring, Vol.9, No.4, pp. 287-323, 2013, DOI:10.32604/sdhm.2013.009.287
Abstract The optimum location of the sensors is a critical issue of any successful Structural Health Monitoring System. Sensor optimization problems encompass mainly three areas of interest: system identification, damage identification and impact identification. The current paper is intended as a review of the state of the art at the year 2012 and going back to 1990. The above topics have been dealt with in separate contexts so far but they contain interesting common elements to be exploited. More >

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24

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ARTICLE

Comparative Analysis of Wavelet Transform for Time-Frequency Analysis and Transient Localization in Structural Health Monitoring

Structural Durability & Health Monitoring, Vol.15, No.1, pp. 1-22, 2021, DOI:10.32604/sdhm.2021.012751
Abstract A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable… More >

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14

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ARTICLE

Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 181-203, 2019, DOI:10.32604/sdhm.2019.00287
Abstract Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however, blade get damaged due to wind gusts, bad weather conditions, unpredictable aerodynamic forces, lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade. It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine. In this paper, a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades. The More >

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8

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ARTICLE

Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

Structural Durability & Health Monitoring, Vol.14, No.2, pp. 127-145, 2020, DOI:10.32604/sdhm.2020.07595
Abstract The state of cutting tool determines the quality of surface produced on the machined parts. A faulty tool produces poor surface, inaccurate geometry and non-economic production. Thus, it is necessary to monitor tool condition for a machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique. Cutting force and vibration signals were acquired to monitor tool condition during machining. A set of four tooling conditions namely healthy, worn flank, broken insert and extended… More >

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7

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ARTICLE

A Fatigue Damage Model for FRP Composite Laminate Systems Based on Stiffness Reduction

Structural Durability & Health Monitoring, Vol.13, No.1, pp. 85-103, 2019, DOI:10.32604/sdhm.2019.04695
Abstract This paper introduces a stiffness reduction based model developed by the authors to characterize accumulative fatigue damage in unidirectional plies and (0/θ/0) composite laminates in fiber reinforced polymer (FRP) composite laminates. The proposed damage detection model is developed based on a damage evolution mechanism, including crack initiation and crack damage progress in matrix, matrix-fiber interface and fibers. Research result demonstrates that the corresponding stiffness of unidirectional composite laminates is reduced as the number of loading cycles progresses. First, three common models in literatures are presented and compared. Tensile viscosity, Young’s modulus and ultimate tensile stress… More >

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ARTICLE

Bending and Rolling Shear Properties of Cross-Laminated Timber Fabricated with Canadian Hemlock

Structural Durability & Health Monitoring, Vol.13, No.2, pp. 227-246, 2019, DOI:10.32604/sdhm.2019.04743
Abstract In this paper, bending performance and rolling shear properties of cross-laminated timber (CLT) panels made from Canadian hemlock were investigated by varied approaches. Firstly, three groups of bending tests of three-layer CLT panels with different spans were carried out. Different failure modes were obtained: bending failure, rolling shear failure, bonding line failure, local failure of the outer layer and mixed failure mode. Deflection and strain measurements were employed to calculate the global and local modulus of elastic (MOE), compared with the theoretical value. In addition, a modified compression shear testing method was introduced to evaluate More >

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ARTICLE

Applying Neural Networks for Tire Pressure Monitoring Systems

Structural Durability & Health Monitoring, Vol.13, No.3, pp. 247-266, 2019, DOI:10.32604/sdhm.2019.07025
Abstract A proof-of-concept indirect tire-pressure monitoring system is developed using artificial neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with MATLAB and Simulink to generate simulated accelerometer output data. Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks (RNN-LSTM) and a convolutional neural network (CNN) developed in Python with Tensorflow. Bayesian Optimization via SigOpt was used to optimize training and model parameters. The predictive accuracy and training speed of the two models with various parameters are compared. Finally, future work More >

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ARTICLE

Ambient Vibration Testings and Field Investigations of Two Historical Buildings in Europe

Structural Durability & Health Monitoring, Vol.14, No.4, pp. 283-301, 2020, DOI:10.32604/sdhm.2020.010564
Abstract In this study, the methodology and results of ambient vibration-based investigations of the historical Tash Mosque in Kosovo and a 3-story historical building in Bulgaria are presented. The investigations include full-scale in situ testing of both structures due to ambient vibrations induced by micro-seismic, wind, traffic, and other human activities. To this aim, Ranger seismometers and Kinemetric products were used. Measurements were performed in both horizontal directions in several points along the structures’ height utilizing a high-speed data acquisition device. All recorded data have been analyzed and processed by the software developed at IZIIS, and More >

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ARTICLE

Global-Local FE Simulation of a Plate LVI Test

Structural Durability & Health Monitoring, Vol.9, No.3, pp. 253-267, 2013, DOI:10.32604/sdhm.2013.009.253
Abstract The aim of the proposed research activity is to investigate on the structural behaviour of laminated composite plates, in order to develop numerical models able to describe their "damage resistance" under low velocity impacts. To achieve these results it is necessary to provide understanding of and ultimately predict the initiation and the progression mechanisms of damage. Since analytical closed-form methods are unable to describe simultaneously different failure modes and experimental testing can give indications only for specific cases, not generally applicable, it is necessary to improve numerical techniques, which on the base of opportunely selected More >

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2079

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ARTICLE

A Deep Learning Based Approach for Response Prediction of Beam-like Structures

Structural Durability & Health Monitoring, Vol.14, No.4, pp. 315-338, 2020, DOI:10.32604/sdhm.2020.011083
Abstract Beam-like structures are a class of common but important structures in engineering. Over the past few centuries, extensive research has been carried out to obtain the static and dynamic response of beam-like structures. Although building the finite element model to predict the response of these structures has proven to be effective, it is not always suitable in all the application cases because of high computational time or lack of accuracy. This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural network and the governing differential equation More >

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