Nwadigo, O; Naismith, N N; Ghaffarianhoseini, A; Ghaffarian Hoseini, A and Tookey, J (2021) Dynamic Bayesian network modelling for predicting adaptability of time performance during time influencing factors disruptions in construction enterprise. Engineering, Construction and Architectural Management, 28(10), pp. 2994-3013. ISSN 0969-9988
Abstract
Purpose: A construction project is complex and requires dynamic modelling of a range of factors that deters time performance because of uncertainty and varying operating conditions. In construction project systems, the system components are the interconnected stages, which are time-dependent. Within the project stages are the activities which are the subsystems of the system components, causing a challenge to the analysis of the complex system. The relationship of construction project time management (CTM) with the construction project time influencing factors (CTFs) and the adaptability of the time-varying system is a key part of project effectiveness. This study explores the relationship between CTM and CTF, including the potentials to add dynamical changes on every project stage. Design/methodology/approach: This study proposed a dynamic Bayesian network (DBN) model to examine the relationship between CTM and CTF. The model investigates the time performance of a construction project that enhances decision-making. First, the paper establishes a model of probabilistic reasoning and directed acrylic graph (DAG). Second, the study tests the dynamic impact (IM) of CTM-CTF on the project stages over a specific time, including the adaptability of time performance during disruptive CTF events. In demonstrating the effectiveness of the model, the authors selected one-organisation-single-location road-improvement project as the case study. Next, the confirmation of the model internal validity relied on conditional probabilities and the project knowledge experts' selected from the case company. Findings: The study produced structural dependencies of CTM and CTF with probability observations at each stage. A predictive time performance analysis of the model at different scenarios evaluates the adaptability of CTM during CTF uncertain events. The case demonstration of the model application shows that CTFs have effects on CTM strategy, creating the observations to help time performance restorations after disruptions. Research limitations/implications: Although the case company experts' panel confirms the internal validity of the results for managing time, the model used conditional probability table (CPT) and project state values from a project contract. A project-wide application then will require multi-case data and data-mining process for generating the CPTs. Practical implications: The study developed a method for evaluating both quantitative and qualitative relationships between CTM and CTF, besides the knowledge to enhance CTM practice and research. In construction, the project team can use model observations to implement time performance restorations after a predictive or reactive disruption, which enhances decision-making. Originality/value: The model used qualitative and qualitative data of a complex system to generate results, bounded by a range of probability distributions for CTM-CTF interconnections during time performance disruptions and restorations. The research explores the approach that can complement the mental CTM-CTF modeling of the project team. The CTM-CTF relationship model developed in this research is fundamental knowledge for future research, besides the valuable insight into CTF influence on CTM.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | complex system; construction time influencing factors; decision-making; dynamic Bayesian network; dynamic modelling; time performance |
| Index terms: | methodology, decision-making, time performance, construction project, adaptability, dynamic modelling, restoration, reasoning, improvement project, strategy, project knowledge, mining, effectiveness, modelling, construction time, project team, time management, influencing factor, case study, validity, bayesian network, probability distribution, complex system |
| Subjects: | research methods, performance management, renovation and retrofit, management, production management, cognitive psychology, probabilistic model, geotechnical engineering, decision analysis, project controls, systems engineering, statistical analysis, user focus, evaluation and assessment methods, data collection methods, risk assessment, modelling and simulation, analytical methods, project delivery, knowledge management |
| Topics: | Digital Applications, Design Practice, Quality Management, Time Control, Business Strategy, Engineering Principles, Information Management, Project Management, Research Practice, Risk 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