Civil Engineering and Data Mining

Data Mining (DM) is a multi-disciplinary field and encompasses techniques from a number of fields, including information techniques, statistical analysis, machine learning (ML), pattern recognition, artificial intelligence (AI) and database management.

Reinforced concrete is a widely used construction material. Its properties depend on the bond between the reinforcing bar and concrete as much as the compressive strength or properties of the reinforcing bar because of component of construction expose to both flexural and bond together compressive loads. The compressive and flexural strength properties of the reinforcing bar are taken as the basis of a construction design. Constructions or buildings are not only exposed to compressive, flexural or tensile loads. Particularly, reinforced constructions are exposed mostly to these loads. In addition to these loads, there are a variety of affects such as the bond or flexural bond and the performance of reinforced concrete structures, which depend on adequate bond strength between the concrete and the rebar. Bond strength is one of the most important properties that control the behavior of reinforced concrete structures. However, the determination of its effects requires special equipment.

To determine the bond properties, the bond characteristics between the concrete and reinforcement are commonly used through pull-out, push-in, and related testing methods. The pull-out test is the easiest and oldest of these tests. The relationships between obtained data cannot always be linear. Sometimes these relationships are non-linear or cannot easily be understood. Quantitative models can be defined using statistical approaches or machine learning based data mining approaches for bond properties.

Data mining (DM), also known as Knowledge Discovery Data (KDD), is the process of analyzing data from different angles and summarizing it into useful information. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, DM is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that examine through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely be generalized to make accurate predictions on future data.

Recently, many have tried to apply optimization models to DM and numerous models have been proposed for classification, clustering, and other DM functionalities which have enhanced both the theoretical foundation and practical applications of DM in different scientific fields, such as social or education science, marketing, communications and engineering science. The DM process can be used to estimate relationships between bond and flexural bond properties and the flexural strength, compressive strength and tensile stress of the rebar. In order to find these properties, algorithms in WEKA (Waikato Environment for Knowledge Analysis) can be used in a DM process.

References: Modeling by data mining process ” Civil Engineering Department, Faculty of Engineering and Architectural, Suleyman Demirel University, Turkey, Department of Construction Education, Faculty of Technical Education, Suleyman Demirel University, Turkey”.