Neural network models for actual duration of Greek highway projects

Titirla, M and Aretoulis, G (2019) Neural network models for actual duration of Greek highway projects. Journal of Engineering, Design and Technology, 17(6), pp. 1323-1339. ISSN 1726-0531

Abstract

Purpose: This paper aims to examine selected similar Greek highway projects to create artificial neural network-based models to predict their actual construction duration based on data available at the bidding stage. Design/methodology/approach: Relevant literature review is presented that highlights similar research approaches. Thirty-seven highway projects, constructed in Greece, with similar type of available data, were examined. Considering each project’s characteristics and the actual construction duration, correlation analysis is implemented, with the aid of SPSS. Correlation analysis identified the most significant project variables toward predicting actual duration. Furthermore, the WEKA application, through its attribute selection function, highlighted the most important subset of variables. The selected variables through correlation analysis and/or WEKA and appropriate combinations of these are used as input neurons for a neural network. Fast Artificial Neural Network (FANN) Tool is used to construct neural network models in an effort to predict projects’ actual duration. Findings: Variables that significantly correlate with actual time at completion include initial cost, initial duration, length, lanes, technical projects, bridges, tunnels, geotechnical projects, embankment, landfill, land requirement (expropriation) and tender offer. Neural networks’ models succeeded in predicting actual completion time with significant accuracy. The optimum neural network model produced a mean squared error with a value of 6.96E-06 and was based on initial cost, initial duration, length, lanes, technical projects, tender offer, embankment, existence of bridges, geotechnical projects and landfills. Research limitations/implications: The sample size is limited to 37 projects. These are extensive highway projects with similar work packages, constructed in Greece. Practical implications: The proposed models could early in the planning stage predict the actual project duration. Originality/value: The originality of the current study focuses both on the methodology applied (combination of Correlation Analysis, WEKA, FannTool) and on the resulting models and their potential application for future projects.

Item Type: Article
Uncontrolled Keywords: attribute selection; highway construction; neural networks; predicting models; project actual duration; Waikato environment for knowledge analysis
Index terms: tunnel, accuracy, artificial neural network, Greece, neural network, literature review, completion time, methodology, landfill, bidding, embankment, package, correlation analysis, sample size, highway construction, duration, land
Subjects: contractual arrangements, data analysis and analytics, artificial intelligence, bidding, modelling and simulation, real estate economics, waste management, research design and methodology, statistical analysis, infrastructure and transport systems, project controls, professional development, Geography, civil engineering, research methods
Topics: Time Control, Urban Studies, Digital Applications, Procurement, Sustainability, Geographical Context, Research Practice, 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