Data Analytics for Numeric Modeling and Its Application to an Offsite Concrete Block Production Operation

Abstract

The majority of real-world systems within the Engineering domain and particularly the construction sector, generate enormous amounts of data every instance of time that they are in operation. This data can be collected from these systems real-time or otherwise using traditional methods or using contemporary techniques such as those that facilitate the implementation of concepts such as the Internet Of Things (IOT). Once gathered into a repository, this data can be utilized for planning, predictive, diagnostic, and other purposes. For this data to be put to such meaningful uses, there are analytics that need to be performed. This paper showcases typical examples of such analytics that generate information that can serve as decision support in a practical setting. First, background information that is necessary to support simple to complex data analytics is presented. This is followed by a case study used to demonstrate how analytics can be performed on data from an offsite concrete block production operation to gain insights into the operation and for diagnostic purposes. To achieve this, probability distributions fit to collected data for each state variable are utilized in a setup Monte Carlo simulation experiment configured to predict concrete production cycle lengths.

Keywords: Data, Analytics, Offsite, Concrete block, Production, Cycle Length, Monte Carlo Simulation