Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective LCCA comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the wholecost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANN is a powerful means to handle non-linear problems and subsequently map relationships between complex input/output data and address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method using MATLAB SOFTWARE; and secondly, spread-sheet optimisation using Microsoft Excel Solver. The best network used 19 hidden nodes, with the tangent sigmoid used as a transfer function for both methods. The results is that in both models, the accuracy of the developed NN model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

Purpose This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects. Design/methodology/approach Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC). Findings The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates. Originality/value The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates.

Purpose-The purpose of this study is to compare the performance of regression and artificial-neuralnetworks (ANNs) methods to estimate the running-cost of building-projects towards improved accuracy. Design/methodology/approach-A data-set of 20 building-projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression-modelling. A connection-weight method is applied to determine the importance of cost factors in the performance of ANNs. Findings-The results illustrate that the value of the coefficient of determination = 99.75% for ANNs model(s), with a value of 98.1% utilising multiple-regression-model(s); secondly, the mean percentage error for ANNs at a testing stage is 0.179, which is less than that of the mean percentage error gained through multipleregression-modelling of 1.28; and thirdly, the average accuracy is 99% for ANNs model(s) and 97% for multiple-regression-model(s). On the basis of these results, it is concluded that an ANNs model is superior to a multiple-regression-model when predicting running-cost of building projects. Research limitations/implications-A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample. Originality/value-This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

Construction projects have numerous variable factors affecting the value of life cycle-cost (LCC) and there is interaction between these factors, leading to complicated process. Therefore the current LCC models suffer from the absence of a standardized and a simple methodology of both the collection data and estimation LCC. Identification of the significant cost factors affecting the output of LCC is important step before embarking upon the collection of progress information. Furthermore, components of construction differ in their cost and time-importance, and thus management effort must be equivalently distributed. The concept of cost significant items (CSIs) is the best method to simplify estimation methodology as well as the collection data of construction projects. In general, The CSIs concept aims to determine the small number of items which represent a constant percentage of the LCC of construction projects. Therefore, this paper aims to present the explanation of each stage of LCC, the current classifications of asset components, the CSIs concept and previous practice of the significant cost items on the construction sector.

A major limitation of Life-Cycle Cost (LCC) estimation/prediction modelling is the current typical reliance only on those factors that can be readily quantified and come easily to hand. While estimation of the cost of the most common labor, material and plant resources receive consideration because of their high visibility factor, there are several non-cost factors (low visibility factors) affecting the estimate that are often overlooked and, it is argued here, require equal consideration in estimation processes that seek optimum accuracy. Unfortunately, such (low-visibility) factors are neglected or ignored by current prediction models. Identification of these non-cost factors (low visibility factors) affects LCC estimate accuracy and can improve estimation process confidence. This paper critically reviews secondary research on identification of these important non-cost factors and subsequently determines their influence on the accuracy level(s) of construction cost estimation.