Predicting construction litigation outcome using particle swarm optimization

Author name used in this publication: Kwokwing ChauSeries: Lecture notes in computer science2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Predicting Construction Litigation Outcome Using Particle Swarm Optimization

Construction claims are normally affected by a large number of complex and interrelated factors. It is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting the outcome of construction claims in Hong Kong in the last 10 years. The results show faster and more accurate results than its counterparts of a benching back-propagation neural network and that the PSO-based network are able to give a successful prediction rate of up to 80%. With this, the parties would be more prudent in pursuing litigation and hence the number of disputes could be reduced significantly.Department of Civil and Environmental EngineeringAuthor name used in this publication: Kwokwing ChauSeries: Lecture notes in computer scienc

Application of a PSO-based neural network in analysis of outcomes of construction claims

Author name used in this publication: K. W. Chau2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A split-step PSO algorithm in predicting construction litigation outcome

Series: Lecture notes in computer science2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Prediction of construction litigation outcome using a split-step PSO algorithm

2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A Split-Step PSO Algorithm in Predicting Construction Litigation Outcome

Abstract. Owing to the highly complicated nature and the escalating cost involved in construction claims, it is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. This paper presents the application of a split-step particle swarm optimization (PSO) model for training perceptrons to predict the outcome of construction claims in Hong Kong. The advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg-Marquardt algorithm in the second step are combined together. The results demonstrate that, when compared with the benchmark backward propagation algorithm and the conventional PSO algorithm, it attains a higher accuracy in a much shorter time

Prediction of construction litigation outcome - A case-based reasoning approach

Series: Lecture notes in computer scienceAuthor name used in this publication: K. W. Chau2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities