Application of Applied AI models in concrete mixture proportion

• Main Authors: Lu, Su-Hsiu, 呂夙修
• Other Authors: Hung, Shih-Lin
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/04998395925296353336

碩士 === 國立交通大學 === 土木工程學系 === 99 === Concrete is one of most utilized construction materials in civil and infrastructural engineering. Concrete is a highly sensitive material to the issues in production process, such as proportioning, mixing, pouring, curing, etc. Among those factors, propositioning is the most important aspect. However, concrete mix, designed based on conventional methods, are not guaranteed to satisfy the required aim. Meanwhile, if the concrete specimen test does not pass, it results in not only wasting cost, but also loss of time. Recently, computer-aided design of concrete mix proportioning is a feasible approach with the aspect of reducing the resource costs and increasing construction efficiency. Based on cost optimization approach, there are, currently, many schemes of computer-aided concrete mix proportioning design. However, cost is not the only aim for concrete mix design. This work attempts to employ K-Means algorithm to analyze the pre-collected database to design concrete mix proposition based on the predefined requirements and provide a diversity of design to satisfy requirements of engineering. In addition, ANN model can generate solutions, if K-Means algorithm cannot find solutions in database. Simulation results reveal that the system is feasible and practicable in concrete mix design.