The principal goal of this AI-centric initiative is to engineer a forward-thinking predictive instrument. It is specifically designed for Construction Companies operating with the capacity to provide cost and time estimations predictions for activities, and to discern potential risks. The implementation of such a tool marks a significant stride in the advancement of the construction supply chain.

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PROJECT HEALTH PREDICTION

In this venture, our foremost aim was to leverage supervised Machine Learning (ML) models trained on a wealth of historical project data. This tool illuminates crucial insights by forecasting not only the cost and schedule, but also risk profiles for projects.

Ultimately, this tool spotlights pivotal elements to help navigate risk factors and foster a proactive approach to addressing potential future challenges.

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PROJECT PERFORMANCE PREDICTION

Our methodology hinges on the deployment of ML to forecast risk profiles associated with projects’ characteristics, performance, and cost overruns. By incorporating diverse features, we pinpoint the central drivers behind key project outcomes with the construction supply chain partners.

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 We have successfully developed cutting-edge predictive models for project cost overrun and performance- that exceed our baseline performance, using iterations and incorporating new features.

Moreover, we are integrating a Natural Language Processing (NLP) model. This implementation enables the categorization of on-site activity descriptions into coherent clusters, helping unearth insightful drivers for performance. These factors include weather conditions, equipment malfunctions, congestion, and noise-related complaints, among others.