Incidents leading to a health and safety risk are a common occurrence in most large-scale Construction & Engineering work sites, which can lead to millions of dollars in damages and lawsuits affecting the financial performance of the overall project. Early detection of these potential issues and proactive interventions could lead to avoiding accidents or safety breaches that can occur in the worksite, which will not only have huge financial benefits but also will ensure timely delivery of projects. We, part of the Construction Engineering Global Business Unit (CEGBU) in Oracle, have applied NLP-based state-of-the-art Machine Learning (ML) models to classify text data from textual construction injury reports as well as correspondence data between construction project participants. The health and safety risk detection sub-system can predict if the text data is associated with (any impending) risks with high accuracy. The sub-system also enables human decision- maker to provide feedback for these correspondences, based on human experience and intuition, in case the prediction(s) made by the subsystem is incorrect. This feedback will later be fed back to the subsystem as (new) labelled training data to improve the prediction accuracy and reinforce the sub-system over time.