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Using AI to generate and iterate schedules based on scope documents - a world first

This paper introduces Schedule Studio, a generative AI technology that transforms how construction schedules are created and kept updated. Leveraging the largest database of construction schedules globally and using plain text inputs, this tool automates the creation and iteration of detailed schedules, making the process faster, more flexible, and more impactful to project decision making. Key advancements in the fields of document processing and generative AI have made this innovation possible.

Practical Implementation of AI-based risk management processes on construction megaprojects

Recently, AI-Schedule Risk Analysis (AI-SRA) has emerged as a groundbreaking approach to construction project risk management. Unlike traditional Quantitative Schedule Risk Analysis (QSRA), AI-SRA leverages machine learning models, trained on extensive historical schedule data, to directly predict activity durations distributions based on data embedded in the schedule.

Data-Driven Schedule Risk Forecasting for Construction Mega-Projects

V Hovhannisyan, P Zachares, A Mosca, C Ledezma, Y Grushka-Cockayne, AACE International Conference 2023

A graph-based approach for unpacking construction sequence analysis to evaluate schedules

Y Hong, H Xie, V Hovhannisyan, I BrilakisAdvanced Engineering Informatics 52, 101625

On Forecasting Project Activity Durations with Neural Networks

P Zachares, V Hovhannisyan, C Ledezma, J Gante, A Mosca International Conference on Engineering Applications of Neural Networks, 103-114

Determining Construction Method Patterns to Automate and Optimise Scheduling–A Graph-based Approach

Y Hong, V Hovhannisyan, H Xie, I Brilakis2021 European Conference on Computing in Construction

How to Calibrate your Neural Network Classifier: Getting True Probabilities from a Classification Model

N Culakova, D Murphy, J Gante, C Ledezma, V Hovhannisyan, A Mosca; Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3499–3500, 2020.