Big Data and Sensor Network for Construction Material Testing

Engineers and contractors need to have a precise understanding of the development progress of concrete strength in the natural environment, which helps to save project time and cost. However, current practice of concrete construction depends on published data, charts and curves from laboratory tests. The data would not show frequently changing environmental conditions in the real world, which can affect concrete quality significantly. The objective of this research is to design a reliable and accurate method to validate test data of the strength developments of concrete specimens in early stages. The approach includes the following tasks: (1) arrange sensors to monitor the temperature data of in-place concrete; (2) record the sensor data automatically; and (3) design the temperature control on a concrete-curing device to keep the curing conditions of the specimens the same as in-place concrete. The Smart and Synchronized Concrete Curing (SSCC) system designed and developed in this research is a big data and sensor network (BDSN) for construction material testing, particularly concrete materials. The data collection lasts 40 days and includes 50 strength reports for concrete specimens. The data analysis includes hypothesis testing, regression, and machine learning. The findings show that the modified measurement method of concrete strength is effective and a reliable management and control system for sample testing and data collection. In addition, the research explores the relationship between environment temperatures and concrete temperatures. This entire research design is applicable to various concrete construction projects