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  • Open Access

    ARTICLE

    Artificial Neural Network (ANN) Approach for Predicting Concrete Compressive Strength by SonReb

    Mario Bonagura, Lucio Nobile*

    Structural Durability & Health Monitoring, Vol.15, No.2, pp. 125-137, 2021, DOI:10.32604/sdhm.2021.015644 - 03 June 2021

    Abstract The compressive strength of concrete is one of most important mechanical parameters in the performance assessment of existing reinforced concrete structures. According to various international codes, core samples are drilled and tested to obtain the concrete compressive strengths. Non-destructive testing is an important alternative when destructive testing is not feasible without damaging the structure. The commonly used non-destructive testing (NDT) methods to estimate the in-situ values include the Rebound hammer test and the Ultrasonic Pulse Velocity test. The poor reliability of these tests due to different aspects could be partially contrasted by using both methods together,… More >

  • Open Access

    ABSTRACT

    Quantitative nondestructive tests of defects with optical methods

    Xide Li

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.20, No.1, pp. 13-14, 2011, DOI:10.3970/icces.2011.020.013

    Abstract The quantitatively assessment of defect characteristic parameters (DCP), such as coordinates, size, embedding depth and type of defects in structures or materials, is becoming increasingly important. One reason is for economy and safety, and the other for a defect in structures may lead to failure while it is growing to a critical size. Therefore, it is possible to estimate both remaining life of structures and extent of the degradation by knowing the dimensions of defects presenting in structures. In this paper, several research topics on quantitative detection of the DCP in our group are reviewed… More >

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