Waste Data Processing Algorithm and Data Collection Integration System (DCIS) in Singular Construction Activities for Aiding the Quantification of Overruns in Construction Process

Project delays wreak havoc on the construction industry, threatening the economies of governments all over the world. There are several studies on the causes and effects of construction delays, but remedies that were meant to offer the results of this phenomenon (such as lean construction) are not being implemented on a large scale. One explanation for this is that delays are generated by time wastes at activity levels, which are rarely addressed in scholarly investigations. Managers rely heavily on experience-based heuristics to determine the duration of activities. Construction activities, on the other hand, are prone to highly unlikely and complex process flows, rendering heuristics unreliable. The reason for this is that there is a slim chance that one project’s construction site conditions and stakeholders will be comparable to the next. As a result, the project management expertise obtained may not be useful in projecting actual project durations and costs in the future. The only viable option would be to establish cost and timing guidelines for individual building operations based on the entire history of projects and employees involved. Globalizing, or at the very least nationalising, heuristic data on delays and wastes might aid in the prediction of future processes in the long term. It is possible to achieve this by demonstrating a method for centralising the construction process into a single data entity at the national level—the Data Collection System (DCIS). Every time a new construction process is activated anywhere inside the DCIS area, the system performs a synchronisation of people and construction site data. The epicentre of this data centre will be the collection of inventory data, material data, labour data, stakeholder data, delay data, time waste data, and so on. The information gleaned via heuristics should then be transformed into mathematical distributions that may be used to forecast future development sites. As the data entering process progresses, this will result in better and better results.

Author(s) Details

Safeer Ali Abbas Ali
National Institute of Technology, Calicut, Kerala, India.

C. Arun
National Institute of Construction Management and Research, Goa, India.

K. Krishnamurthy
National Institute of Technology, Calicut, Kerala, India.

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