NLP for Automated Discovery and Assessment of Dominant Construction and Maintenance Work Order Activities in MEP Projects

The installation of Mechanical, Electrical, and Plumbing (MEP) systems for building facilities accounts for up to 40% of the total construction, while Operation and Maintenance activities constitute about 65% of the Facilities Management costs where MEP works are part of the main contributors. In this vein, most companies generate and manage vast amounts of construction and operations data in diverse Information Systems, but they lack data-driven insights to better plan, analyze, and control costs and profit related to construction and maintenance work orders. To tackle this problem, this paper presents a method that helps general contractors and facility managers to automatically discover dominant MEP-related activities from construction data involving 99k work orders of Operation and Maintenance activities and 118k construction work orders executed in several projects across North America. The proposed method involves (1) data preprocessing and automated identification of frequent activities by applying text-mining and Natural Language Processing (NLP) techniques on free-form text activity descriptions; and (2) a quantitative analysis of significant activities in terms of cost and profit. Results include the identification of dominant activities and providing quantitative insights on their occurrence frequency. The presented data-driven approach has the capability to identify items in high demand and those carrying significant profitability or cost implications for general contractors and facility managers.