Economic Sustainability of Building and Construction Projects Based on Artificial Intelligence Techniques


  • Bashir Hussein Yahaya Lecturer, Department of Quantity Surveying, Federal Polytechnic Nasarawa, Nigeria
  • Abdullahi Alhassan Ahmed Lecturer, Department of Estate Management, Federal Polytechnic Nasarawa, Nigeria
  • Bibiana Ometere Anikajogun Lecturer, Department of Urban and Regional Planning, Federal Polytechnic Nasarawa, Nigeria



Artificial Intelligence, Building, Construction, Industry, Machine Learning


Artificial intelligence (AI) has been shown to be an effective replacement for conventional modelling approaches. AI is a subfield of computer science that develops software and tools that mimic human intelligence. AI offers advantages over traditional methods for handling ambiguous circumstances. In addition, AI-based solutions can successfully replace testing when identifying engineering design parameters, saving a lot of time and resources. AI can also increase computer efficiency, decrease mistake rates, and speed up decision-making. Recently, there has been a lot of interest in machine learning (ML), a new area of cutting-edge intelligent methods for use in structural engineering. Consequently, this work presents a study on the economic management of building and construction projects based on creating ML techniques. It begins with an overview of the value of applying AI techniques in building and construction industry. The analysis of the prediction of reinforced concrete’s compressive strength while taking cost into account is then done using empirical data based on a case study. Accordingly, the findings showed that the support vector regression (SVR) and k-Nearest Neighbour (KNN) intelligence techniques are helpful in the construction business for controlling the strength of concrete based on sustainable cost reduction.


S. K. Baduge et al., “Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications,” Automation in Construction, Vol. 141, pp. 104440, 2022.

J. Murphy, S. Brown, and G. Harris, “UKCP additional land products: probabilistic projections of climate extremes,” Met Office scientific report (Exeter, UK), 2020.

L. C. Felius, B. G. Pollet, and J. J. Lamb, “Introduction to energy efficiency in buildings,” in Energy-Smart Buildings: Design, construction and monitoring of buildings for improved energy efficiency: IOP Publishing, 2020.

A. N. Beskopylny et al., “Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression,” Applied Sciences, Vol. 12, No. 21, pp. 10864, 2022.

J. R. Martf-Vargas, “Practical Approach for Assessing Lightweight Aggregate Potential for Concrete Performance,” ACI Materials Journal, Vol. 112, No. 1, pp. 173-175, 2015.

A. Kumar et al., “Compressive strength prediction of lightweight concrete: Machine learning models,” Sustainability, Vol. 14, No. 4, pp. 2404, 2022.

A. M. Onaizi, G. F. Huseien, N. H. A. S. Lim, M. Amran, and M. Samadi, “Effect of nanomaterials inclusion on sustainability of cement-based concretes: A comprehensive review,” Construction and Building Materials, Vol. 306, pp. 124850, 2021.

G. Ozcan, Y. Kocak, and E. Gulbandilar, “Estimation of compressive strength of BFS and WTRP blended cement mortars with machine learning models,” Comput. Concr, Vol. 19, pp. 275-282, 2017.

Y. Qian and G. De Schutter, “Enhancing thixotropy of fresh cement pastes with nanoclay in presence of polycarboxylate ether superplasticizer (PCE),” Cement and Concrete Research, Vol. 111, pp. 15-22, 2018.

W. Rao, L. Zhang, Z. Zhang, and Z. Wu, “Noise-suppressing chaos generator to improve BER for DCSK systems,” in 2017 IEEE International Conference on Communications (ICC), IEEE, pp. 1-6, 2017.

S. K. U. Rehman et al., “Assessment of rheological and piezoresistive properties of graphene based cement composites,” International Journal of Concrete Structures and Materials, Vol. 12, No. 1, pp. 1-23, 2018.

J. PP. M. Rinchon, “Strength durability-based design mix of self-compacting concrete with cementitious blend using hybrid neural network-genetic algorithm,” IPTEK Journal of Proceedings Series, Vol. 3, No. 6, 2017.

Ł. Sadowski, M. Piechówka-Mielnik, T. Widziszowski, A. Gardynik, and S. Mackiewicz, “Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust,” Journal of cleaner production, Vol. 212, pp. 727-740, 2019.

P. P. Samui, “Multivariate adaptive regression spline (Mars) for prediction of elastic modulus of jointed rock mass,” Geotechnical and Geological Engineering, Vol. 31, pp. 249-253, 2013.

M. M. Moein et al., “Predictive models for concrete properties using machine learning and deep learning approaches: A review,” Journal of Building Engineering, pp. 105444, 2022.

S. N. Qasem et al., “Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates,” Engineering Applications of Computational Fluid Mechanics, Vol. 13, No. 1, pp. 177-187, 2019.




How to Cite

Yahaya, B. H., Ahmed, A. A., & Anikajogun, B. O. (2023). Economic Sustainability of Building and Construction Projects Based on Artificial Intelligence Techniques. The Asian Review of Civil Engineering, 12(1), 34–40.