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

Authors:
Kwok-wing Chau
Affiliations:
Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 8
Cited 3

The basic ideas in neural networks

Communications of the ACM
Real-Time Prediction of Water Stage with Artificial Neural Network Approach

AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Predicting construction litigation outcome using particle swarm optimization

IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
River stage forecasting with particle swarm optimization

IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Evaluation of several algorithms in forecasting flood

IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Long-Term prediction of discharges in manwan hydropower using adaptive-network-based fuzzy inference systems models

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Long-Term prediction of discharges in manwan reservoir using artificial neural network models

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

Computers and Structures
A software tool for teaching of particle swarm optimization fundamentals

Advances in Engineering Software
Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The nature of construction claims is highly complicated and the cost involved is high. It will be advantageous if the parties to a dispute may know with some certainty how the case would be resolved if it were taken to court. The recent advancements in artificial neural networks may render 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 split-step particle swarm optimization (PSO) model is applied to train perceptrons in order to predict the outcome of construction claims in Hong Kong. It combines the advantages of global search capability of PSO algorithm in the first step and the local convergence of back-propagation algorithm in the second step. It is shown that, through a real application case, its performance is much better than the benchmark backward propagation algorithm and the conventional PSO algorithm.