{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T10:44:43Z","timestamp":1780569883778,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T00:00:00Z","timestamp":1603238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The cost, time and scope of a construction project are key parameters for its success. Thus, predicting these indices is indispensable. Correct and accurate prediction of cost throughout the progress of a project gives project managers the chance to identify projects that need revision in their schedules in order to result in the maximum benefit. The aim of this study is to minimize the shortcomings of the Earned Value Management (EVM) method using an Artificial Neural Network (ANN) and multiple regression analysis in order to predict project cost indices more precisely. A total of 50 road construction projects in Fars Province, Iran, were selected for analysis in this research. An ANN model was used to predict the projects\u2019 cost performance indices, thereby creating a more accurate symmetry between the predicted and actual cost by considering factors that influence project success. The input data of the ANN model were analysed in MATLAB software. A multiple regression model was also used as another analytical tool to validate the outcome of the ANN. The results showed that the ANN model resulted in a lower Mean Squared Error (MSE) and a greater correlation coefficient than both the traditional EVM model and the multiple regression model.<\/jats:p>","DOI":"10.3390\/sym12101745","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T02:01:42Z","timestamp":1603418502000},"page":"1745","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Improving the Results of the Earned Value Management Technique Using Artificial Neural Networks in Construction Projects"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6443-3030","authenticated-orcid":false,"given":"Amirhossein","family":"Balali","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Shiraz Branch, Islamic Azad University, Shiraz 5-71993, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Valipour","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Shiraz Branch, Islamic Azad University, Shiraz 5-71993, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1734-3216","authenticated-orcid":false,"given":"Jurgita","family":"Antucheviciene","sequence":"additional","affiliation":[{"name":"Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, LT\u201310223 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3685-7754","authenticated-orcid":false,"given":"Jonas","family":"\u0160aparauskas","sequence":"additional","affiliation":[{"name":"Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, LT\u201310223 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/978-981-13-3317-0_59","article-title":"Early cost estimation of highway projects in India using artificial neural network","volume":"Volume 25","author":"Das","year":"2019","journal-title":"Sustainable Construction and Building Materials Lecture Notes in Civil Engineering"},{"key":"ref_2","unstructured":"Wideman, R.M. (1995). Cost Control of Capital Projects and the Project Cost Management System Requirements: A Handbook for Owners, Architects, Engineers, and All Those Involved in Project Management of Constructed Facilities, AEW Services, BiTech Publishers."},{"key":"ref_3","first-page":"1","article-title":"Forecasting the cost of structure of infrastructure projects utilizing artificial neural network model (highway projects as case study)","volume":"10","author":"Aidan","year":"2017","journal-title":"Indian J. Sci. Technol."},{"key":"ref_4","unstructured":"Turochy, R.E., Hoel, L.A., and Doty, R.S. (2001). Highway Project Cost Estimating Methods Used in the Planning Stage of Project Development, Virginia Transportation Research Council."},{"key":"ref_5","first-page":"1036","article-title":"Cost estimation of highway projects in developing countries: Artificial neural network approach","volume":"6","author":"Sodikov","year":"2005","journal-title":"J. East. Asia Soc. Transp. Stud."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15","DOI":"10.35784\/bud-arch.2320","article-title":"Earned value method as a tool for project control","volume":"3","author":"Czernigowska","year":"2008","journal-title":"Bud. Archit."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1177\/875697280303400403","article-title":"Earned value project management method and extensions","volume":"34","author":"Anbari","year":"2003","journal-title":"Proj. Manag. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.jclepro.2019.05.079","article-title":"Earned Green Value Management for Project Management: A systematic review","volume":"230","author":"Koke","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.ijproman.2017.12.002","article-title":"Conditions of success for earned value analysis in projects","volume":"36","author":"Bryde","year":"2018","journal-title":"Int. J. Proj. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.1016\/j.eswa.2014.12.007","article-title":"A comparison of the performance of various project control methods using earned value management systems","volume":"42","author":"Colin","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.ijproman.2016.10.014","article-title":"Extensions of earned value management: Using the earned incentive metric to improve signal quality","volume":"35","author":"Kerkhove","year":"2017","journal-title":"Int. J. Proj. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/j.ijproman.2017.12.003","article-title":"A model to control environmental performance of project execution process based on greenhouse gas emissions using earned value management","volume":"36","author":"Abdi","year":"2018","journal-title":"Int. J. Proj. Manag."},{"key":"ref_13","first-page":"1","article-title":"Exploring earned value management in the Spanish construction industry as a pathway to competitive advantage","volume":"20","author":"Sutrisna","year":"2020","journal-title":"Int. J. Constr. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.autcon.2018.11.030","article-title":"Using real project schedule data to compare earned schedule and earned duration management project time forecasting capabilities","volume":"99","author":"Martens","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_15","first-page":"70","article-title":"Artificial neural networks for construction management: A review","volume":"1","author":"Kulkarni","year":"2017","journal-title":"J. Soft Comput. Civ. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1111\/j.1467-8667.1989.tb00026.x","article-title":"Perceptron learning in engineering design","volume":"4","author":"Adeli","year":"1989","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1111\/0885-9507.00219","article-title":"Neural networks in civil engineering: 1989\u20132000","volume":"16","author":"Adeli","year":"2001","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_18","first-page":"2450370","article-title":"Estimation of costs and durations of construction of urban roads using ANN and SVM","volume":"2017","author":"Vujkov","year":"2017","journal-title":"Complexity"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/S0263-7863(97)00007-0","article-title":"A neural network application to subcontractor rating in construction firms","volume":"16","author":"Albino","year":"1998","journal-title":"Int. J. Proj. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/S0360-1323(00)00029-9","article-title":"Comparative study of artificial neural networks and multiple regression analysis for predicting hoisting times of tower cranes","volume":"36","author":"Leung","year":"2001","journal-title":"Build. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ijproman.2005.08.001","article-title":"Predicting project performance through neural networks","volume":"24","author":"Cheung","year":"2006","journal-title":"Int. J. Proj. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10031","DOI":"10.1016\/j.eswa.2011.02.014","article-title":"Neural networks in economic analyses of wastewater systems","volume":"38","author":"Vouk","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_23","first-page":"175","article-title":"Estimation of recycling capacity of multi-storey building structures using artificial neural networks","volume":"10","year":"2013","journal-title":"Acta Polytech. Hung."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1016\/j.ijproman.2015.09.002","article-title":"Prediction of outcome of construction dispute claims using multilayer perceptron neural network model","volume":"33","author":"Chaphalkar","year":"2015","journal-title":"Int. J. Proj. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5972620","DOI":"10.1155\/2019\/5972620","article-title":"Application of artificial neural network (s) in predicting formwork labour productivity","volume":"2019","author":"Golnaraghi","year":"2019","journal-title":"Adv. Civ. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9343","DOI":"10.1007\/s00521-019-04443-y","article-title":"Cost estimation in road construction using artificial neural network","volume":"32","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"112811","DOI":"10.1016\/j.eswa.2019.07.028","article-title":"A comprehensive study on the use of artificial neural networks in wearable fall detection systems","volume":"138","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.eswa.2016.04.027","article-title":"Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction","volume":"59","author":"Kim","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.eswa.2017.07.020","article-title":"Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations","volume":"88","author":"Kocadagli","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.eswa.2018.01.048","article-title":"Neural network modeling for a two-stage production process with versatile variables: Predictive analysis for above-average performance","volume":"100","author":"Kwon","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112896","DOI":"10.1016\/j.eswa.2019.112896","article-title":"Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach","volume":"140","author":"Bandara","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yazdani-Chamzini, A., Zavadskas, E.K., Antucheviciene, J., and Bausys, R. (2017). A model for shovel capital cost estimation, using a hybrid model of multivariate regression and neural networks. Symmetry, 9.","DOI":"10.3390\/sym9120298"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Juszczyk, M., and Le\u015bniak, A. (2019). Modelling construction site cost index based on neural network ensembles. Symmetry, 11.","DOI":"10.3390\/sym11030411"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"AlHares, E.F.T., and Budayan, C. (2019). Estimation at completion simulation using the potential of soft computing models: Case study of construction engineering projects. Symmetry, 11.","DOI":"10.3390\/sym11020190"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"715","DOI":"10.3846\/jcem.2019.10534","article-title":"Forecasting of sports fields construction costs aided by ensembles of neural networks","volume":"25","author":"Juszczyk","year":"2019","journal-title":"J. Civ. Eng. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.3846\/20294913.2017.1303648","article-title":"Assessing collusion risks in managing construction projects using artificial neural network","volume":"24","author":"Shan","year":"2018","journal-title":"Technol. Econ. Dev. Econ."},{"key":"ref_37","first-page":"177","article-title":"Factors influencing time and cost overruns in road construction projects: Addis Ababa, Ethiopian scenario","volume":"5","author":"Tadewos","year":"2018","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/01446190903468923","article-title":"Construction cost analysis under uncertainty with correlated cost risk analysis model","volume":"28","year":"2010","journal-title":"Constr. Manag. Econ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Plebankiewicz, E. (2018). Model of predicting cost overrun in construction projects. Sustainability, 10.","DOI":"10.3390\/su10124387"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Habibi, M., Kermanshachi, S., and Safapour, E. (2018, January 2\u20134). Engineering, procurement and construction cost and schedule performance leading indicators: State-of-the-art review. Proceedings of the Construction Research Congres, New Orleans, LA, USA.","DOI":"10.1061\/9780784481271.037"},{"key":"ref_41","unstructured":"Flyvbjerg, B., Holm, M.S., and Buhl, S. (2004). Cost Underestimation in Public Works Projects: Error or Lie?, Aalborg University, Department of Development and Planning."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Heravi, G., and Mohammadian, M. (2019). Investigating cost overruns and delay in urban construction projects in Iran. Int. J. Constr. Manag., 1\u201311.","DOI":"10.1080\/15623599.2019.1601394"},{"key":"ref_43","unstructured":"Moura, H.M.P., Teixeira, J.M.C., and Pires, B. (2007, January 14\u201317). Dealing with cost and time in the Portuguese construction industry. Proceedings of the CIB World Building Congress, Cape Town, South Africa."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"116361","DOI":"10.1016\/j.jmatprotec.2019.116361","article-title":"Construction of three-dimensional extrusion limit diagram for magnesium alloy using artificial neural network and its validation","volume":"275","author":"Bai","year":"2020","journal-title":"J. Mater. Process Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"73","DOI":"10.4186\/ej.2020.24.1.73","article-title":"Using artificial neural network for selecting type of subcontractor relationships in construction project","volume":"24","author":"Nov","year":"2020","journal-title":"Eng. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1166\/jnn.2020.17235","article-title":"Analysis the compressive strength of flue gas desulfurization gypsum using artificial neural network","volume":"20","author":"Jang","year":"2020","journal-title":"J. Nanosci. Nanotechnol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11709-018-0489-z","article-title":"Modeling of bentonite\/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine","volume":"13","author":"Ghanizadeh","year":"2019","journal-title":"Front. Struct. Civ. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chesnokov, A., Mikhailov, V., and Dolmatov, I. (2019, January 20\u201322). Evaluation of adverse factors acting on a pre-stressed wire rope structure by means of artificial neural network. Proceedings of the 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA), Lipetsk, Russia.","DOI":"10.1109\/SUMMA48161.2019.8947494"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.conbuildmat.2019.03.119","article-title":"Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates","volume":"209","author":"Hammoudi","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"ref_50","first-page":"21","article-title":"An artificial neural network based phrase network construction method for structuring facility error types","volume":"19","author":"Roh","year":"2018","journal-title":"J. Internet Comput. Serv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Johnson, J., Hossain-McKenzie, S., Bui, U., Etigowni, S., Davis, K., and Zonouz, S. (2017, January 17\u201320). Improving power system neural network construction using modal analysis. Proceedings of the 19th International Conference on Intelligent System Application to Power Systems (ISAP), San Antonio, TX, USA.","DOI":"10.1109\/ISAP.2017.8071367"},{"key":"ref_52","unstructured":"Veelenturf, L.P. (1995). Analysis and Applications of Artificial Neural Networks, Prentice-Hall, Inc."},{"key":"ref_53","unstructured":"Beale, M.H., Hagan, M.T., and Demuth, H.B. (2010). Neural Network Toolbox, User\u2019s Guide MathWorks."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1139\/cjfr-2017-0396","article-title":"Assessing components of the model-based mean square error estimator for remote sensing assisted forest applications","volume":"48","author":"McRoberts","year":"2018","journal-title":"Can. J. For. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Vanhoucke, M. (2009). Measuring Time: Improving Project Performance Using Earned Value Management, Springer Science & Business Media.","DOI":"10.1007\/978-1-4419-1014-1"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1007\/s11269-009-9573-4","article-title":"Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions","volume":"24","author":"Zadeh","year":"2010","journal-title":"Water Resour. Manag."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Datta, D., Agarwal, S., Kumar, V., Raj, M., Ray, B., and Banerjee, A. (2019, January 26\u201329). Design of current mode sigmoid function and hyperbolic tangent function. Proceedings of the International Symposium on VLSI Design and Test, Sapporo, Japan.","DOI":"10.1007\/978-981-32-9767-8_5"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Namin, A.H., Leboeuf, K., Muscedere, R., Wu, H., and Ahmadi, M. (2009, January 24\u201327). Efficient hardware implementation of the hyperbolic tangent sigmoid function. Proceedings of the IEEE International Symposium on Circuits and Systems, Taipei, Taiwan.","DOI":"10.1109\/ISCAS.2009.5118213"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Leboeuf, K., Namin, A.H., Muscedere, R., Wu, H., and Ahmadi, M. (2008, January 11\u201313). High speed VLSI implementation of the hyperbolic tangent sigmoid function. Proceedings of the Third International Conference on Convergence and Hybrid Information Technology, Busan, Korea.","DOI":"10.1109\/ICCIT.2008.131"},{"key":"ref_60","unstructured":"Lin, C.-W., and Wang, J.-S. (2008, January 18\u201321). A digital circuit design of hyperbolic tangent sigmoid function for neural networks. Proceedings of the IEEE International Symposium on Circuits and Systems, Seattle, WA, USA."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"79","DOI":"10.4316\/AECE.2018.03011","article-title":"Implementation of high speed tangent sigmoid transfer function approximations for artificial neural network applications on FPGA","volume":"18","author":"Koyuncu","year":"2018","journal-title":"Adv. Electr. Comput. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5826","DOI":"10.1016\/j.eswa.2010.11.041","article-title":"Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method","volume":"38","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Gao, Y., and Cowling, M. (2019). Introduction to Panel Data, Multiple Regression Method, and Principal Components Analysis Using Stata: Study on the Determinants of Executive Compensation\u2014A Behavioral Approach Using Evidence from Chinese Listed Firms, SAGE Publications.","DOI":"10.4135\/9781526495983"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5958","DOI":"10.1016\/j.eswa.2010.11.027","article-title":"Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils","volume":"38","author":"Yilmaz","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1504\/IJSSE.2019.100338","article-title":"Multi-objective multi-customer project network: Visualising interdependencies and influences","volume":"9","author":"Stumpe","year":"2019","journal-title":"Int. J. Syst. Syst. Eng."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/10\/1745\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:25:21Z","timestamp":1760178321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/10\/1745"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,21]]},"references-count":65,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["sym12101745"],"URL":"https:\/\/doi.org\/10.3390\/sym12101745","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,21]]}}}