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

HYDROLOGICAL FORECASTING USING NEURAL NETWORKS

Operational planning of water resources systems like reservoirs and power plants calls for realtime or on-line forecasting of runoff and river stage. Most of the real-time forecasting models used in the past are of the distributed type, where the forecasts are made at several locations within a catchment area. In situations where the information is needed only at specific sites in a river basin, and needs to be more accurate, the time and effort required in developing and implementing such complicated models may not be justified. Simpler neural network (NN) forecasts may therefore seem attractive as an alternative. The present study demonstrates the application of NNs to real-time forecasting of hourly flood runoff and daily river stage, as well as to the prediction of rainfall sufficiency for India. The study showed the capability of NNs in all of these applications. In many situations they performed better than the statistical models

River Stage Forecasting with Particle Swarm Optimization

Abstract. An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks, such as very slow convergence and easily getting stuck in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting real-time water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown from the verification simulations that faster and more accurate results can be acquired.