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“Rapid and Accurate Identification of Concrete Surface Cracks via a Lightweight & Efficient YOLOv3 Algorithm” looks into the identification of cracks in concrete structures for service performance evaluation. This article proposes a tailored deep-learning algorithm MDN-YOLOv3 for rapidly and accurately identifying crack locations and patterns. Based on three retrofit techniques and a novel backbone network for YOLOv3, the improved algorithm achieved multi-scale feature fusion and robust feature extraction in a much smaller size and higher speed than traditional algorithm, which means MDN-YOLOv3 is more suitable for application on mobile devices.

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  • Open AccessOpen Access

    ARTICLE

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

    Haoan Gu1, Kai Zhu1, Alfred Strauss2, Yehui Shi3,4, Dragoslav Sumarac5, Maosen Cao1,*
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 363-380, 2024, DOI:10.32604/sdhm.2024.042388 - 05 June 2024
    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 >

  • Open AccessOpen Access

    ARTICLE

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

    Pshtiwan Shakor1,2,*, Nadarajah Gowripalan3, Paul Rocker4
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 395-408, 2024, DOI:10.32604/sdhm.2024.049366 - 05 June 2024
    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 >

    Graphic Abstract

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

  • Open AccessOpen Access

    ARTICLE

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

    Hantao Huang, Zhifang Zhang*, Zhenhua Wu, Yao Liu
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 409-423, 2024, DOI:10.32604/sdhm.2024.048597 - 05 June 2024
    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 >

  • Open AccessOpen Access

    ARTICLE

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

    Yang Li1, Zikang Zheng1,*, Jiangkun Zhang2
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 425-444, 2024, DOI:10.32604/sdhm.2024.046584 - 05 June 2024
    (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 >

    Graphic Abstract

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

  • Open AccessOpen Access

    ARTICLE

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

    Guangfei Jia*, Yanchao Meng, Zhiying Qin
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298 - 05 June 2024
    (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 >

    Graphic Abstract

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

  • Open AccessOpen Access

    ARTICLE

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

    Chengpeng Zhang, Junfeng Shi*, Caiping Huang
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 465-483, 2024, DOI:10.32604/sdhm.2024.048705 - 05 June 2024
    (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 >

  • Open AccessOpen Access

    ARTICLE

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

    Yue Cao1,2, Longsheng Bao1, Xiaowei Zhang1,*, Zhanfei Wang1, Bingqian Li1
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 485-503, 2024, DOI:10.32604/sdhm.2024.049698 - 05 June 2024
    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 >

    Graphic Abstract

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

  • Open AccessOpen Access

    ARTICLE

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

    Xiangmiao Wan, Yan Tan*, Xiong Long
    Structural Durability & Health Monitoring, Vol.18, No.4, pp. 505-524, 2024, DOI:10.32604/sdhm.2024.049363 - 05 June 2024
    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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