Bokor, O (2022) Improving labour productivity in construction. A hybrid machine learning approach. PhD thesis, University of Northumbria at Newcastle, UK.
Abstract
Achieving less than ideal productivity is a problem the construction industry faces in most advanced countries, including the UK. One way to change this is to improve on-site execution by, for example, more accurate planning of construction operations. Despite continuous efforts for automation, mechanisation, and off-site production, the construction industry can still be considered labour-intensive. Therefore, understanding labour productivity and the factors influencing it is vital to better planning. Owing to their versatility, durability, long service life, and being low maintenance, bricklaying works are ubiquitous, especially in housing and public projects, for example, schools. These operations are also especially labour-intensive. Consequently, an examination of bricklaying works is important for better planning and management of most construction projects. Ultimately, any gains in this operation could lead to an overall increase in site-based productivity. The aim of the research project is to provide a better understanding of the bricklaying process and how it can be modelled, descriptively and normatively, to find a modelling approach that allows for a better examination of the effects of various factors on bricklaying productivity. A number of factors influence on-site productivity. This research project focuses on those that are known in advance, in the pre-planning phase of the construction projects. These are the worker and wall characteristics. To analyse bricklaying operations, a hybrid model is created. The effects of the above-mentioned factors on labour productivity are investigated with the help of the artificial neural network component, while the discrete-event simulation part models the process of block- and bricklaying. The model is built and tested with the help of real-life data collected at two construction projects by conducting a traditional work study. When the productivity rates were measured, note was made of the bricklayer working on the course, and the wall section where they worked. Site supervisors filled in the questionnaires asking about operative characteristics, while the wall characteristics were determined based on the drawings and specifications. The resulting model can be used to provide more accurate productivity rate predictions for more precise time and cost estimates, and improved project planning in bricklaying.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Florez Perez, L; Pesce, G; Gledson, B and Osborne, A |
| Uncontrolled Keywords: | UK; artificial neural network; automation; bricklayer; bricklaying; construction operations; durability; labour productivity; learning; machine learning; neural network; productivity; project planning; schools; service life; simulation; work study |
| Index terms: | operative, work study, modelling, construction industry, cost estimate, automation, labour productivity, off-site production, site supervisor, construction operation, durability, productivity, service life, project planning, housing, machine learning, questionnaire, public project, construction project, artificial neural network, drawing, neural network, face, bricklayer, specification |
| Subjects: | asset management, manufacturing engineering, production management, automation and robotics, infrastructure engineering, psychology, construction operations, analytical methods, construction type, practitioner, industry analysis, management, contractual condition, technical documentation, modelling and simulation, artificial intelligence, material degradation and durability, financial and cost management, data collection methods, control systems |
| Topics: | Design Practice, Digital Applications, Organizational Design, Site Management, Contract Administration, Cost Management, Business Strategy, Research Practice, Construction Materials, Roles and Professions, Construction Technology, Project Management, Engineering Principles |
| Descriptive scope: | 4 PCTA |
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