Kurpinska Marzena, Kułak Leszek
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland.
Faculty Applied Physics and Mathematics, Gdansk University of Technology, 80-233 Gdansk, Poland.
Materials (Basel). 2019 Jun 22;12(12):2002. doi: 10.3390/ma12122002.
Lightweight concrete (LWC) is a group of cement composites of the defined physical, mechanical, and chemical performance. The methods of designing the composition of LWC with the assumed density and compressive strength are used most commonly. The purpose of using LWC is the reduction of the structure's weight, as well as the reduction of thermal conductivity index. The highest possible strength, durability and low thermal conductivity of construction materials are important factors and reasons for this field's development, which lies largely in modification of materials' composition. Higher requirements for construction materials are related to activities aiming at environment protection. The purpose of the restrictions is the reduction of energy consumption and, as a result, the reduction of CO emission. To limit the scope of time-consuming and often high-cost laboratory works necessary to calibrate models used in the test methods, it is possible to apply Artificial Neural Networks (ANN) to predict any of the concrete properties. The aim of this study is to demonstrate the applicability of this tool for solving the problems, related to establishing the relation between the choice of type and quantity of lightweight aggregates and the porosity, bulk density and compressive strength of LWC. For the tests porous lightweight Granulated Expanded Glass Aggregate (GEGA) and Granulated Ash Aggregate (GAA) have been used.
轻质混凝土（LWC）是一组具有特定物理、机械和化学性能的水泥基复合材料。最常用的方法是设计具有假定密度和抗压强度的LWC组成。使用LWC的目的是减轻结构重量，以及降低热导率指数。建筑材料尽可能高的强度、耐久性和低热导率是该领域发展的重要因素和原因，这在很大程度上取决于材料组成的改性。对建筑材料的更高要求与旨在环境保护的活动有关。这些限制的目的是减少能源消耗，从而减少二氧化碳排放。为了限制校准测试方法中使用的模型所需的耗时且通常成本高昂的实验室工作范围，可以应用人工神经网络（ANN）来预测混凝土的任何性能。本研究的目的是证明该工具在解决与确定轻质骨料的类型和数量选择与LWC的孔隙率、堆积密度和抗压强度之间的关系相关问题方面的适用性。对于测试，使用了多孔轻质颗粒膨胀玻璃骨料（GEGA）和颗粒灰骨料（GAA）。
