首页> 外文会议>International RILEM workshop on life prediction and aging management of concrete structures >ARTIFICIAL INTELLIGENCE METHODS AND ANALYSIS OF STRUCTURE IN EVALUATION OF HARDENED CONCRETE QUALITY
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ARTIFICIAL INTELLIGENCE METHODS AND ANALYSIS OF STRUCTURE IN EVALUATION OF HARDENED CONCRETE QUALITY

机译:淬硬混凝土质量评价的人工智能方法与结构分析

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Evaluation of quality of concrete in elements of building or civil engineering structures is needed in procedures of their acceptance, repair, modification or forensic analysis. There are various specialised test methods dedicated to particular characteristics of concrete, but of real importance is evaluation of the general quality of the material. Conventional methods of checking the concrete in such structures are either approximate or laborious, sometimes really expensive. Testing of various properties other than compressive strength may need long time and specialized equipment. A procedure is suggested for evaluation of the current state of hardened concrete based on structural analysis of small samples of material taken from selected elements of the construction, which is supplemented by a general assessment of the entire element, formulated during inspection on site. The main data taken into account in the presented experiments enclose quantitative characteristics resulting from image analysis of the microstructure, from microhardness and other mechanical tests. Certain components of the database concern also qualitative classification and/or subjective statements of experienced observers. The processing of the data is done applying methods originating in so called Artificial Intelligence, like Artificial Neural Networks, (ANNs), or Machine Learning, (ML).
机译:在接受,修复,修改或法医分析的程序中,需要评估建筑物或土木工程结构元素中的混凝土的质量。有各种专业的测试方法专用于具体的具体特征,但实际重要性是评估材料的一般质量。在这种结构中检查混凝土的常规方法是近似或艰苦的,有时非常昂贵。测试除抗压强度之外的各种性能可能需要长时间和专用设备。提出了一种方法,用于评估基于从施工的所选元素中取出的小型材料的结构分析来评估硬化混凝土的当前状态,这通过对现场检查期间制定的整个元素的一般性评估补充。在所提出的实验中考虑的主要数据包围由微硬度和其他机械测试的微观结构的图像分析产生的定量特性。数据库的某些组成部分涉及经验丰富的观察员的定性分类和/或主观陈述。对数据的处理是在源自所谓人工智能的方法的应用,如人工神经网络,(ANNS)或机器学习,(ML)。

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