A novel data-driven tool to improve construction schedule accuracy

Talbot, Barry (2021) A novel data-driven tool to improve construction schedule accuracy. Doctoral thesis, Anglia Ruskin University.

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Abstract

Construction schedules are frequently criticised for inaccuracy and poor project performance, including unplanned and preventable costs and delays. Currently, project planning involves the use of rules of thumb and memories of the outcome of previous similar tasks, leading to optimism bias where the predicted duration is shorter than the actual duration. Reinforced concrete (RC) frames are recognised as critical components of tall buildings, with the gap in practice identified as the inaccurate scheduling of RC frame structures. This research aims to produce a novel tool to enhance construction project management by improving construction schedule accuracy in reinforced concrete (RC) frame buildings, with current scheduling practices and site productivity investigated and the tool developed and validated. A questionnaire survey was undertaken to investigate the phenomenon of inaccurate scheduling found in practice, followed by a series of seven interviews to further probe the results of the questionnaire. Six recently completed projects were then examined to determine discrepancies between the predicted and achieved schedule durations. The findings of the data collection were analysed quantitatively, with the duration to install formwork and reinforcement determined to be the most critical tasks to schedule accuracy in RC frame structures. A tool (Calchas) was then created to predict future task durations by collecting and analysing productivity-related data to identify the most likely task duration using reference class forecasting. A novel algorithm was developed to collect and store project performance metrics, where the data is interpreted by a sequence of code and stored in a structured, searchable planning knowledge database. A second novel algorithm was then created with associated code developed to extract relevant data from the database and forecast task durations with a view to increasing the accuracy of the construction schedule and enhancing the planning decisions made.

Item Type: Thesis (Doctoral)
Additional Information: Accessibility note: If you require a more accessible version of this thesis, please contact us at arro@aru.ac.uk
Keywords: Planning, Project Management, Reference Class Forecasting, RC Frame
Faculty: Theses from Anglia Ruskin University
Depositing User: Lisa Blanshard
Date Deposited: 05 Jan 2022 15:19
Last Modified: 17 Jan 2022 15:23
URI: https://arro.anglia.ac.uk/id/eprint/707233

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