Mohsen, Osama (2021) A machine learning approach to predict production time in industrialized building construction. PhD thesis, University of Alberta, Canada.
Abstract
Abstract: Industrialized building construction is an effective approach for improving the performance and management of construction projects by offering higher quality products, minimized environmental impacts, and improved schedule predictability. The industrialized building construction approach integrates manufacturing principles and techniques into the construction industry where products (in this case, building components) are built in a controlled factory environment and then transported, in sequence, to the construction site for the final assembly. With the marked growth of available data gathered as part of the daily operations in industrialized building construction facilities, one promising approach is to utilize machine learning techniques to identify valid, useful, and previously unknown patterns from historical data. These techniques are used to leverage the use of data to accurately predict the production cycle time. This thesis presents a framework to investigate potential improvements in estimating cycle time in industrialized building construction facilities where accurate prediction of cycle time can improve the quality of production planning and scheduling. The goal is to assist production/project managers to mitigate any delays and implement alternative actions should there be any unexpected delays due to factory operations as well as to manage the capacity and workload of the fabrication facility more efficiently. The results of two case studies reveal that machine learning approaches can be successfully applied to accurately predict cycle time in different subsectors of industrialized building construction. The factors that significantly affect the prediction accuracy are (1) the physical characteristics and tracking information of products, (2) the engineered features that are generated to capture real-time loading conditions of the job shop, and (3) the lookback timeframe used for model training and validation. To examine the effect of shop loading features more thoroughly, a discrete-event simulation model is developed to investigate the various features that can be captured in the real system, but for practical reasons are not presently captured in the data, and to investigate their benefit in terms of improving the predictive accuracy. The major contribution of the research presented in this dissertation is that it provides practitioners in industrialized building construction with a roadmap to utilize their available production data for accurate estimates of delivery dates. The research results are also expected to be a point of reference for future studies in the academic field and for the industrialized building construction industry.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Mohamed, Yasser and Al-Hussein, Mohamed |
| Uncontrolled Keywords: | industrialized building construction; prefabricated construction; modular construction; production time; cycle time; time prediction; wall panels; pipe spools; scheduling; construction 4.0; simulation; data mining; machine learning; knowledge discovery in databases; feature engineering; shop loading; activity durations |
| Index terms: | workload, production planning, accuracy, construction site, scheduling, estimating, building component, machine learning, environmental impact, validation, practitioner, physical characteristic, construction project, modular construction, database, time prediction, project manager, case study, data mining, construction 4.0, future study, fabrication, loading, dissertation, industrialized building, duration, construction industry, estimate |
| Subjects: | architectural elements, manufacturing engineering, production management, building construction, digital engineering, research dissemination and communication, practitioner, environmental impact, operations research, construction operations, management, professional development, material properties and characteristics, project controls, data management, industry analysis, research design and methodology, profession, data collection methods, work location, project delivery, financial and cost management, data science, artificial intelligence |
| Topics: | Sustainability, Engineering Principles, Project Management, Site Management, Time Control, Human Resources, Digital Applications, Design Practice, Construction Technology, Roles and Professions, Construction Materials, Information Management, Research Practice, Cost Management |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here