Open Access Open Access  Restricted Access Subscription or Fee Access

Analyzing Cost and Time Objectives in the Construction Projects Using Artificial Neural Network


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irece.v13i2.21124

Abstract


The most important factor of success in every project is the level of achieving the cost and time objectives. The decision about the initiating of any new project is mostly made based on the estimated time and budget. To achieve an accurate estimation of cost and time in a project, criteria and success indicators should be defined properly to be used for the calculation of the required cost and time of the project. Therefore, by comparing the criteria and indicators in different projects, the decision-making process can be calibrated to improve the accuracy of the estimation process. In the present study, based on characteristics of different factors of projects related to time and cost and other criteria, a suitable formulation between time and cost ratio is presented. Also, using a smart method (artificial neural networks), a model for the prediction of the time and cost of the project is developed. The model can be applied in future projects based on available data of completed projects and optimized status of estimated time and cost can be observed.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords


Construction Project Management; Estimating Project Budge; Scheduling Problem; Time-Cost Trade-Off; Artificial Neural Networks

Full Text:

PDF


References


F. Ning, Y. Shi, M. Cai, W. Xu, X. Zhang, Manufacturing cost estimation based on the machining process and deep-learning method, Journal of Manufacturing Systems, Volume 56, Pages 11-22, ISSN 0278-6125, 2020.
https://doi.org/10.1016/j.jmsy.2020.04.011

E. Rodríguez, G. Pezzotta, R. Pinto, D. Romero, A comprehensive description of the Product-Service Systems' cost estimation process: An integrative review, International Journal of Production Economics, Volume 221, 2020, 107481, ISSN 0925-5273.
https://doi.org/10.1016/j.ijpe.2019.09.002

C. Post, N. Wentingmann, C. Bramsiepe, G. Schembecker, Using design spaces for more accurate cost estimation during early engineering phases, Chemical Engineering Research and Design, Volume 153, , Pages 592-602, ISSN 0263-8762, 2020.
https://doi.org/10.1016/j.cherd.2019.11.011

G. Ling, N. Elliot, J. C. Burstein, D. F. McCaffrey, C. A. MacArthur, S. Holtzman, Writing motivation: A validation study of self-judgment and performance, Assessing Writing, Volume 48, 100509, ISSN 1075-2935, 2021.
https://doi.org/10.1016/j.asw.2020.100509

Q. Zhao, J. Bai, L. Huang, B. Gu, Q. Lu, Z. Gao, A review of methodologies and success indicators for coastal wetland restoration, Ecological Indicators, Volume 60, Pages 442-452, ISSN 1470-160X, 2016.
https://doi.org/10.1016/j.ecolind.2015.07.003

M. Sharifi, A. Manian, The study of the success indicators for pre-implementation activities of Iran's E-Government development projects, Government Information Quarterly, Volume 27, Issue 1, Pages 63-69, ISSN 0740-624X, 2010.
https://doi.org/10.1016/j.giq.2009.04.006

M, Bou Orm, J. Jeunet, Time Cost Quality Trade-off Problems: A survey exploring the assessment of quality, Computers & Industrial Engineering, Volume 118, Pages 319-328, ISSN 0360-8352, 2018.
https://doi.org/10.1016/j.cie.2018.01.012

J. Jeunet, M. Bou Orm, Optimizing temporary work and overtime in the Time Cost Quality Trade-off Problem, European Journal of Operational Research, Volume 284, Issue 2, Pages 743-761, ISSN 0377-2217, 2020.
https://doi.org/10.1016/j.ejor.2020.01.013

S. Mungle, L. Benyoucef, S. Son, M.K. Tiwari, A fuzzy clustering-based genetic algorithm approach for time-cost-quality trade-off problems: A case study of highway construction project, Engineering Applications of Artificial Intelligence, Volume 26, Issue 8, Pages 1953-1966, ISSN 0952-1976, 2013.
https://doi.org/10.1016/j.engappai.2013.05.006

R. Rezaee, R. Vakilinezhad, J. Haymaker, Parametric framework for a feasibility study of zero-energy residential buildings for the design stage, Journal of Building Engineering, Volume 35, 101960, ISSN 2352-7102, 2021.
https://doi.org/10.1016/j.jobe.2020.101960

E. S. Abdelall, L. Alawneh, M. Eldakroury, A manufacturability-based assessment and design modification support tool. Journal of Applied Research and Technology, 18(6), 390-409, 2021.
https://doi.org/10.22201/icat.24486736e.2020.18.6.1366

A. Sidani, F. Matoseiro Dinis, J. Duarte, L. Sanhudo, D. Calvetti, J. Santos Baptista, A. Soeiro, Recent tools and techniques of BIM-based augmented reality: A systematic review. Journal of Building Engineering, 42, 2021.
https://doi.org/10.1016/j.jobe.2021.102500

D. M. Stol, E. A. B. Over, I. F. Badenbroek, M. Hollander, M. M. J. Nielen, R. A. Kraaijenhagen, G. A. de Wit, Cost-effectiveness of a stepwise cardiometabolic disease prevention program: Results of a randomized controlled trial in primary care. BMC Medicine, 19(1), 2021.
https://doi.org/10.1186/s12916-021-01933-6

C. T.Ouédraogo, S. A. Vosti, K. R. Wessells, C. D. Arnold, M. T. Faye, S. Y. Hess, Out-of-pocket costs and time spent attending antenatal care services: A case study of pregnant women in selected rural communities in zinder, niger. BMC Health Services Research, 21(1), 2021.
https://doi.org/10.1186/s12913-020-06027-2

M. Abdul Nabi, I. H. El-Adaway, Understanding the key risks affecting cost and schedule performance of modular construction projects. Journal of Management in Engineering, 37(4), 2021.
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000917

A. Mróz, The role of product cost deployment in the early product management methodology within the WCM system - A case study. Management and Production Engineering Review, 11(4), 13-24, 2021.
https://doi.org/10.24425/mper.2020.136116

E. Kraft, D. Keles, W. Fichtner, Modeling of frequency containment reserve prices with econometrics and artificial intelligence. Journal of Forecasting, 39(8), 1179-1197, 2020.
https://doi.org/10.1002/for.2693

P. He, K. Li, P. N. R. Kumar, An enhanced branch-and-price algorithm for the integrated production and transportation scheduling problem. International Journal of Production Research, 2021.
https://doi.org/10.1080/00207543.2021.1876941

A. Schilling, A. Maier, R. Gerum, C. Metzner, P. Krauss, Quantifying the separability of data classes in neural networks. Neural Networks, 139, 278-293, 2021.
https://doi.org/10.1016/j.neunet.2021.03.035

G. P. Davis, G. E. Katz, R. J. Gentili, J. A. Reggia, Compositional memory in attractor neural networks with one-step learning. Neural Networks, 138, 78-97, 2021.
https://doi.org/10.1016/j.neunet.2021.01.031

A. Apicella, F. Donnarumma, F. Isgrò, R. Prevete, A survey on modern trainable activation functions. Neural Networks, 138, 14-32, 2021.
https://doi.org/10.1016/j.neunet.2021.01.026

B. C. Lines, R. Kakarapalli, P. H. D. Nguyen, Does Best Value Procurement Cost More than Low-Bid? A Total Project Cost Perspective, International Journal of Construction Education and Research, 2020.
https://doi.org/10.1080/15578771.2020.1777489

B. R. R. Boaretto, C. Manchein, T. L. Prado, S. R. Lopes, The role of individual neuron ion conductances in the synchronization processes of neuron networks. Neural Networks, 137, 97-105, 2021.
https://doi.org/10.1016/j.neunet.2021.01.019

R. Royen, L. Denis, Q. Bolsee, P. Hu, A. Munteanu, MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets. Neural Networks, 137, 43-53, 2021.
https://doi.org/10.1016/j.neunet.2021.01.015

M. T. Hagan, H. B. Demuth, M. Beale, Neural network design. Boston: PWS Publishing Company, 1996.

A. Papagiannopoulos, P. Clausen, F. Avellan, How to teach neural networks to mesh: Application on 2-D simplicial contours. Neural Networks, 136, 152-179, 2021.
https://doi.org/10.1016/j.neunet.2020.12.019

H. Wang, Q. Fu, H. Wang, P. Baxter, J. Peng, S. Yue, A bioinspired angular velocity decoding neural network model for visually guided flights. Neural Networks, 136, 180-193, 2021.
https://doi.org/10.1016/j.neunet.2020.12.008

L. Li, Z. Zhang, J. Lu, Artificial fly visual joint perception neural network inspired by multiple-regional collision detection. Neural Networks, 135, 13-28, 2021.
https://doi.org/10.1016/j.neunet.2020.11.018

E. Avuçlu, F. Başçiftçi, Novel approaches to determine age and gender from dental x-ray images by using multiplayer perceptron neural networks and image processing techniques. Chaos, Solitons and Fractals, 120, 127-138, 2019.
https://doi.org/10.1016/j.chaos.2019.01.023

A. Zaafouri, M. Sayadi, F. Fnaiech, Printed arabic character recognition using local energy and structural features. Paper presented at the 2nd International Conference on Communications Computing and Control Applications, CCCA 2012, 2012.
https://doi.org/10.1109/CCCA.2012.6417862

Demuth, H., Beale, M., Neural network toolbox for use with MATLAB, user's guide version 4, neural network toolbox user's guide, copyright 1992 - 2002, Math Works, Inc.

K. Lin, W. Chen, Automatic faulted recognition of electrical system in a local scrubber using infrared images. WSEAS Transactions on Circuits and Systems, 4(3), 164-169, 2005.

K. Lin, W. Chen, Automatic faulted recognition of electrical system in a local scrubber using infrared images. WSEAS Transactions on Circuits and Systems, 4(1), 1-6.

Y.Wang, C. Yu, H. Chan, Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. International Journal of Project Management, Volume 30, p. 470-478, 2012.
https://doi.org/10.1016/j.ijproman.2011.09.002

J. P. Barnard, C. Aldrich, Diagnostic monitoring of internal combustion engines by use of independent component analysis and neural networks. Paper presented at the Proceedings of the International Joint Conference on Neural Networks, 2 869-872, 2003.

A. Jiang, Y Zhu, Life Cycle Cost Analysis of Residential Grid-Connected Solar Photovoltaic Systems in Florida, International Journal of Construction Education and Research, 7:1, 71-81, 2011.
https://doi.org/10.1080/15578771.2010.526443

B. A. K. S. Perera, C.S.R. Perera, C. Jayalath, Contractor's Perspective on Key Performance Indicators of Cost Control in Asian, Middle Eastern, and European Construction Projects, International Journal of Construction Education and Research, 2020.
https://doi.org/10.1080/15578771.2020.1842567

L. Pinky Devi, S. Palaniappan, Life cycle energy analysis of a low-cost house in India, International Journal of Construction Education and Research, 15:4, 256-275, 2019.
https://doi.org/10.1080/15578771.2018.1476935

B. C. Lines, K. T. Sullivan, K. C. Hurtado, J. Savicky, Planning in Construction: Longitudinal Study of Pre-Contract Planning Model Demonstrates Reduction in Project Cost and Schedule Growth, International Journal of Construction Education and Research, 11:1, 21-39, 2015.
https://doi.org/10.1080/15578771.2013.872733

M. Arafa, M. Alqedra, Early stage cost estimation of buildings construction projects using ANN. Journal of Artifical Intelligence, 4(1), pp. 63-75, 2011.
https://doi.org/10.3923/jai.2011.63.75

M.-Y. Cheng, H.-C. Tsai, E. Sudjono, Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Systems with Applications 37, p. 4224-4231, 2010.
https://doi.org/10.1016/j.eswa.2009.11.080

S. Doğan, Using machine learning techniques for early cost estimation of structural systems of buildings, İZMİR: İzmir Institute of Technologyin, 2005.

I. ElSawy, H. Hosny, M. Abdel Razek, A Neural Network Model for Construction Projects Site Overhead Cost Estimating in Egypt. International Journal of Computer Science Issues (IJCSI), 8(1), 2011.

M. Gunaydın, Z. Dogan, A neural network approach for early cost estimation of structural systems of buildings. International Journal of Project Management, Volume 22, p. 595-602, 2004.
https://doi.org/10.1016/j.ijproman.2004.04.002

G.-H. Kim, S.-H. An, K.-I. Kang, Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, February, Volume 39, p. 1235 - 1242, 2004.
https://doi.org/10.1016/j.buildenv.2004.02.013

R. Sonmez, Range estimation of construction costs using neural networks with bootstrap prediction intervals. Expert Systems with Applications, Issue 38, p. 9913- 9917, 2011.
https://doi.org/10.1016/j.eswa.2011.02.042

Y. Wang, C. Yu, H. Chan, Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. International Journal of Project Management, Volume 30, p. 470-478, 2012.
https://doi.org/10.1016/j.ijproman.2011.09.002

M.A. El-Gafy, M. PE, Construction Time-cost Trade-off Analysis Using Ant System, International Journal of Construction Education and Research, 3:2, 99-108, 2007.
https://doi.org/10.1080/15578770701429456


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize