Complexity

Volume 2018, Article ID 7952434, 11 pages

https://doi.org/10.1155/2018/7952434

## ANN Based Approach for Estimation of Construction Costs of Sports Fields

Correspondence should be addressed to Michał Juszczyk; lp.ude.kp.tibwzi@kyzczsujm

Received 29 September 2017; Accepted 13 February 2018; Published 14 March 2018

Academic Editor: Andrej Soltesz

Copyright © 2018 Michał Juszczyk et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach.

#### 1. Introduction

The results presented in this paper are part of a broad research, in which the authors participate, aiming to develop tools for fast cost estimates, dedicated to the construction industry. The main aim of this paper is to present the results of the investigations on the applicability of artificial neural networks (ANNs) in the problem of estimating the total cost of construction works in the case of sports fields as specific facilities. The authors propose herein a new approach based on ANNs for estimating construction costs of sports fields.

##### 1.1. Cost Estimation in Construction Projects

Cost estimation is a key issue in construction projects. Both underestimation and overestimation of costs may lead to a failure of a construction project. The use of different tools and techniques in the whole project life cycle should provide information about costs to the participants of the project and support a complex decision-making process. In general, cost estimating methods can be classified as follows [1, 2]:(i)Qualitative cost estimating:(1)Cost estimating based on heuristic methods(2)Cost estimating based on expert judgments(ii)Quantitative cost estimating:(1)Cost estimating based on statistical methods(2)Cost estimating based on parametric methods(3)Cost estimating based on nonparametric methods(4)Cost estimating based on analogous/comparative methods(5)Cost estimating based on analytical methods.

The expectations of the construction industry are to shorten the time necessary to predict costs, whilst on the other hand, the estimates must be reliable and accurate enough. There are worldwide publications in which the authors report the research results which respond to these expectations. The examples of the use of a regression analysis (based on both parametric and nonparametric methods) are as follows: application of multivariate regression to predict accuracy of cost estimates on the early stage of construction projects [3], implementation of linear regression analysis methods to predict the cost of raising buildings in the UK [4], proposal and discussion of the construction cost estimation method which combines bootstrap and regression techniques [5], and application of boosting regression trees in preliminary cost estimates for school building projects in Korea [6]. Another mathematical tool for which some examples can be given is fuzzy logic, for example, implementation of fuzzy logic for parametric cost estimation in construction building projects in Gaza Strip [7] or proposal and presentation of a fuzzy risk assessment model for estimating a cost overrun risk rating [8]. Case based reasoning (CBR) is also an approach which can be found in the publications dealing with the construction cost issue, for example, implementation of the CBR method improved by analytical hierarchy process (AHP) for the purposes of cost estimation of residential buildings in Korea [9] or the use of the case based reasoning in cost estimation of adapting military barracks also in Korea [10]. The examples of the publications which report and discuss the applications of artificial neural networks in the field of cost estimation and cost analyses in the construction process are presented in the next subsection.

##### 1.2. Artificial Neural Networks Cost Estimation in Construction Projects

Artificial neural networks (ANNs) can be defined as mathematical structures and their implementations (both hardware and software), whose mode of action is based on and inspired by nervous systems observed in nature. In other words, ANNs are tools of artificial intelligence which have the ability to model data relationships with no need to assume a priori the equations or formulas which bind the variables. The networks come in wide variety depending on their structures, way of processing signals, and applications. The theory in this subject is widely presented in the literature (e.g., [11–15]). Main applications of ANN can be mentioned as follows (cf., e.g., [11, 12, 15]): prediction, approximation, control, association, classification and pattern recognition, associating data, data analysis, signal filtering, and optimization. ANNs features which make them beneficial in cost estimating problems (in particular for cost estimating in construction) are as follows:(i)Applicability in regression problems where the relationships between the dependent and many independent variables are difficult to investigate(ii)Ability to gain knowledge in the automated training process(iii)Ability to build and store the knowledge on the basis of the collected training patterns (real-life examples)(iv)Ability of knowledge generalization; predictions can be made for the data which have not been presented to the ANNs during a training process.

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. In some previous works, the authors presented the ANN applications for conceptual cost estimation of residential buildings in Poland [22–24] and estimation of overhead cost in construction projects in Poland [25, 26].

##### 1.3. Justification for Research

It needs to be emphasized that, despite a number of publications reporting research projects on the use of artificial neural networks in cost analyses and cost estimation in construction, each of the problems is specific and unique. Each of such problems requires an individual approach and investigation due to distinct conditions, determinants, and factors that influence the costs of construction projects. An individual approach to cost estimation in construction is primarily due to specificity of the facilities, including sports fields. The costs of a sport field are significant not only for the construction stage but also later in terms of its maintenance. The decisions made about the size, functionality, and quality are crucial for the future use and operational management of sport fields. The success in investigation of ANNs applicability in the problem will allow proposing a new approach for estimation of the construction cost of sport fields. The new approach, based on the advantages offered by neural networks, will allow predicting the total construction cost of sport fields much faster than with traditional methods; moreover, it will give the possibility of checking many variants and their influence on the cost in a very short time.

#### 2. Formulation of the Problem and Research Framework

##### 2.1. General Assumptions

The general aim of the research was to develop a model that supports the process of estimating construction costs of sports fields. The authors decided to investigate implementation of ANNs for the purpose of mapping multidimensional space of cost predictors into a one-dimensional space of construction costs. In a formal notation, the problem can be defined generally as follows:

where is sought-for function of several variables, is input of the function , which consists of vectors , where variables represent cost predictors characteristic of sports fields as construction objects, and is a set of values which represent construction costs of sports fields.

In the statistical sense, the problem comes down to solving a regression problem and estimating of a relationship between the cost predictors being independent variables belonging to the set and constructions cost of a sports field being dependent variable belonging to the set . According to the methodology in cost estimating based on statistical methods, one can distinguish between two main approaches: estimating based on parametric methods and estimating based on nonparametric methods (cf. [1, 2, 25]). Both methods rely on the real-life data, that is, representative samples of cost predictors values and related construction costs values. In the case of the use of parametric methods function is assumed a priori and the structural parameters of the model are estimated. On the other hand, nonparametric methods are based on fitting the function to the data. According to the assumptions made for the research presented in this paper, the sought-for function was supposed to be implemented implicitly by ANN.

A general framework of the adopted research strategy is depicted in Figure 1.