Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

Abstract

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.

Artificial Neural Networks in Construction Engineering and Management

“…Prediction accuracy is one key attributes of ANN over traditional methods which adds to its popularity and usage. Accuracy levels of most ANN based applications in prediction and forecasting can get to as high as 98% over test samples as evident in Geiger et al (1998), Alqahtani and Whyte (2013) and Bala et al (2014). These performances are recorded on the bases of minimum errors over test samples as measured by MSE, MAE, MAPE and SSE.…”

ANN Based Approach for Estimation of Construction Costs of Sports Fields

“…Some examples of ANN applications reported for a range of cost estimating and cost analyses in construction are replication of past cost trends in highway construction and estimation of future costs trends in this field in the state of Louisiana, USA [16], computation of the whole life cost of construction with the use of the concept of cost significant items in Australia [17], prediction of the total structural cost of construction projects in the Philippines [18], estimation of site overhead costs in the dam project in Egypt [19], prediction of the cost of a road project completion on the basis of bidding data in New Jersey, USA [20], and cost estimation of building structural systems in Turkey [21]. The authors of this paper also have their contribution in studies on the use of ANN in cost estimation problems in construction.…”

Evaluation of non-cost factors affecting the life cycle cost: an exploratory study

“…It will aid the choice of optimum techniques and approaches for operation and maintenance and “best” disposal of unneeded components. Previous studies identify limitations for using the concept of LCC as: a lack of predictive data; absence of a standardised methodology; and incomprehension of the complex process (Alqahtani and Whyte, 2013; Asjad et al , 2013; Minne and Crittenden, 2015). …”

“…incomprehension of the complex process (Alqahtani and Whyte, 2013; Asjad et al , 2013; Minne and Crittenden, 2015).…”

“…In addition, current estimation methods (probability analyses and statistical tools) have several disadvantages, such as being time-consuming, being costly in method, the need for accurate information to improve the result, seeming to be problematic when applied to the early stages of the asset life cycle because of a lack of information and inaccurate results especially when applied on a system level (Alqahtani and Whyte, 2013).…”