Search

Abstract: Most consultant selection models stress the importance of past performance. However, there are so far very few studies on evaluating consultant's performance. It hampers the whole process of the selection model. This paper tries to establish a systematic approach in developing a consultant's performance evaluation model for the evaluation of the performance of cost estimators in the construction industry of Hong Kong. Nominal group technique is adopted in identifying the decision criteria for the evaluation, and a reliability interval method (RIM) is developed to assess the importance weighting of each criterion. The RIM allows statistical analysis and fuzzy assessment of the weight. The results report the weights of each decision criterion and its subcriterion in evaluating cost estimator's performance. Interestingly, the results show that traditional functions of cost estimator are the least treasured from the clients. Proactive and professional advice is considered to be much more important. The study is highly relevant to industry practitioners in assessing performance of costs estimators, as well as to researchers in further developing the consultant selection model. CE Database subject headings: Cost control; Performance evaluation; Consulting services; Consultants; Construction industry.

A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems. Index Terms--Fuzzy adaptive resonance theory (ART), general regression neural network (GRNN), general regression neural network with fuzzy ART clustering (GRNNFA), noisy data regression.

The prediction of air quality parameters is of great interest in environmental studies today due to the health impact caused by airborne pollutants [e.g., sulfur dioxide (S[O.sub.2]); nitrogen oxide (N[O.sub.x]); nitric oxide (NO); nitrogen dioxide (N[O.sub.2]); carbon monoxide (CO); respirable suspended particulates (RSPs), etc.] in urban areas. Artificial neural networks are regarded as a reliable and cost-effective method for prediction tasks. The work reported here develops an improved neural network model which combines both the principal component analysis (PCA) technique and the radial basis function (RBF) network to analyze and predict the pollutant data recorded. In the study, PCA is used to reduce and orthogonalize the original variables. The variables treated are then used as input vectors in a RBF neural network model to forecast the pollutant levels, e.g., the RSP level in the downtown area of Hong Kong. This improved method is evaluated based on hourly time series RSP concentrations collected at the Causeway Bay roadside gaseous monitoring station in Hong Kong during 1999. The simulation results show the effectiveness of the model. For high-dimensional input vectors including simpler network architecture and faster learning speed without compromising the generalization capability of the network, the proposed algorithm has advantages over traditional RBF network learning. DOI: 10.1061/(ASCE)0733-9372(2002)128:12(1146) CE Database keywords: Air pollution; Neural networks; Hong Kong; Pollutants.