Mensah, I; Adjei-Kumi, T and Nani, G (2016) Duration determination for rural roads using the principal component analysis and artificial neural network. Engineering, Construction and Architectural Management, 23(5), pp. 638-656. ISSN 0969-9988
Abstract
Purpose Determining the duration for road construction projects represents a problem for construction professionals in Ghana. The purpose of this paper is to develop an artificial neural network (ANN) model for determining the duration for rural bituminous surfaced road projects. Design/methodology/approach Data for 22 completed bituminous surfaced road projects from the Department of Feeder Roads (rural road agency) were collected and analyzed using the principal component analysis (PCA) and ANN techniques. The data collected were final payment certificates which contained payment bill of quantities (BOQ) of work items executed for the selected completed road projects. The executed quantities in the BOQ were the total quantities of work items for site clearance, earthworks, in-situ concrete, reinforcement, formwork, gravel sub-base/base, bitumen, road line markings and furniture, length of road and actual durations for each of the completed projects. The PCA was first employed to reduce the data in order to identify a smaller number of variables (or significant quantities) that constitute 81.58 percent of the total variance of the collected data. The ANN was then used to develop the network using the identified significant quantities as input variables and the actual durations as output variables. Findings The coefficient of correlation (R) and determination (R2) as well as the mean absolute percentage error (MAPE) obtained show that construction professionals can use the developed ANN model for determining duration. The study shows that the best neural network is the multi-layer perceptron with a structure 3-38-1 based on a back propagation feed forward algorithm. The developed network produces good results with an MAPE of 17.56 percent or an average accuracy of 82.44 percent. Research limitations/implications Apart from the fact that the sample size was small, the developed model does not incorporate the implications of other likely factors that may affect contract duration. Practical implications The outcome of this study is to help construction professionals to fix realistic contract duration for road construction projects before signing a contract. Such realistic contract duration would help reduce time overruns as well as the payment of liquidated and ascertained damages by contractors for late completion. Originality/value This paper proposes an alternative way of determining the duration for road construction projects using the total quantities of work items in a final payment BOQ. The approach is based on the PCA and ANN model of quantities of work items of completed road projects.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | principal component analysis; road projects; artificial neural network; bill of quantities; duration; final payment certificate |
| Index terms: | earthwork, neural network, damages, accuracy, artificial neural network, methodology, agency, variance, feed forward, time overrun, formwork, back propagation, sample size, final payment certificate, Ghana, bitumen, construction professional, reinforcement, in-situ, principal component analysis, road construction, duration, bills of quantities, road project |
| Subjects: | civil engineering, research methods, algorithms, Geography, infrastructure engineering, professional development, project controls, payment, measurement and scaling, sociology, building construction, statistical analysis, research design and methodology, dispute resolution, control systems, construction methods, modelling and simulation, building materials, artificial intelligence, financial and cost management |
| Topics: | Contract Administration, Time Control, Legal Issues, Digital Applications, Construction Technology, Cost Management, Geographical Context, Research Practice, Construction Materials, Engineering Principles, Information Management |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here