Improving construction projects and reducing risk by using artificial intelligence

Improving construction projects and reducing risk by using artificial intelligence

Автор: Milena Senjak Pejić, Mirjana Terzić, Dragana Stanojević, Igor Peško, Vladimir Mučenski

Журнал: Social Informatics Journal @socialinformaticsjournal

Статья в выпуске: 1 vol.2, 2023 года.

Бесплатный доступ

Architectural, engineering and construction (AEC) industry has large amounts of data, the analysis of which can predict risks such as project delays, project budget overruns, low resource use efficiency, environmental damage, reduced safety, and the like. Research has shown that 98% of construction projects are realized with the contracted budget and deadline exceeded. This paper aims to show the significant potential and possibilities of applying artificial intelligence (AI) in the AEC industry, pointing out the benefits and obstacles faced by the AEC industry during the implementation of AI. The benefits of applying AI in the AEC industry relate to increasing efficiency, reducing costs, increasing productivity, increasing safety, better planning and providing timely and accurate information. The key obstacles that hinder the implementation of AI in practice are high implementation costs, incomplete data, job loss due to automation, mistrust of AI technology, cyber vulnerability, and lack of understanding field of AI.

Еще

AEC industry, artificial intelligence, construction technology, AI opportunities

Короткий адрес: https://sciup.org/170203983

IDR: 170203983   |   DOI: 10.58898/sij.v2i1.33-40

Список литературы Improving construction projects and reducing risk by using artificial intelligence

  • Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299. https://doi.org/10.1016/j.jobe.2021.103299
  • Alsakka, F., Haddad, A., Ezzedine, F., Salami, G., Dabaghi, M., & Hamzeh, F. (2023). Generative design for more economical and environmentally sustainable reinforced concrete structures. Journal of Cleaner Production, 387, 135829. https://doi.org/10.1016/j.jclepro.2022.135829
  • An, Y., Li, H., Su, T., & Wang, Y. (2021). Determining Uncertainties in AI Applications in AEC Sector and their Corresponding Mitigation Strategies. Automation in Construction, 131, 103883. https://doi.org/10.1016/j.autcon.2021.103883
  • Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440. https://doi.org/10.1016/j.autcon.2022.104440
  • Baker, H., Hallowell, M. R., & Tixier, A. J.-P. (2020). AI-based prediction of independent construction safety outcomes from universal attributes. Automation in Construction, 118, 103146. https://doi.org/10.1016/j.autcon.2020.103146
  • Beckage, J. A., Parziale, D. J. (2021). Why The Construction Industry Is Being Impacted By Cyberat-tacks, And What To Do About It. 2021 Surety Bonding and Construction Risk Management Conference. Accessed date: 30.06.2023. https://www.agc.org/sites/default/files/Galleries/enviro_members_file/CLE%20Paper_%20Cyber%20Attacks%20and%20the%20Construction%20Industry.pdf
  • Changali, S., A. Mohammad, A., Nieuwland, M. (2015). The construction productivity imperative. McKinsey Productivity Sciences Center. Accessed date: 29.06.2023. https://www.mckinsey.com/~/media/McKinsey/Industries/Capital%20Projects%20and%20Infrastruc-tu-re/Our%20Insights/The%20construction%20productivity%20imperative/The%20construction%20productivity%20imperative.pdf
  • Egwim, C. N., Alaka, H., Toriola-Coker, L. O., Balogun, H., & Sunmola, F. (2021). Applied artificial intel-ligence for predicting construction projects delay. Machine Learning with Applications, 6, 100166. https://doi.org/10.1016/j.mlwa.2021.100166
  • Elfahham, Y. (2019). Estimation and prediction of construction cost index using neural networks, time series, and regression. Alexandria Engineering Journal, 58(2), 499–506. https://doi.org/10.1016/j.aej.2019.05.002
  • Emaminejad, N., & Akhavian, R. (2022). Trustworthy AI and robotics: Implications for the AEC industry. Automation in Construction, 139, 104298. https://doi.org/10.1016/j.autcon.2022.104298
  • García de Soto, B., Turk, Ž., Maciel, A., Mantha, B., Georgescu, A., & Sonkor, M. S. (2022). Under-standing the Significance of Cybersecurity in the Construction Industry: Survey Findings. Journal of Construction Engineering and Management, 148(9). https://doi.org/10.1061/(asce)co.1943-7862.0002344
  • Haynes, J. P. (2023). Building a Cybersecurity Foundation to Protect Construction Firms from Ran-somware Attacks. Esentire. Accessed date: 30.06.2023. https://www.esentire.com/blog/building-a-cybersecurity-foundation-to-protect-construction-firms-from-ransomware-attacks
  • Kim, G.-H., An, S.-H., & Kang, K.-I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), 1235–1242. https://doi.org/10.1016/j.buildenv.2004.02.013
  • Lin, J. J., & Golparvar-Fard, M. (2017). Proactive Construction Project Controls via Predictive Visual Data Analytics. Computing in Civil Engineering 2017. https://doi.org/10.1061/9780784480830.019
  • Nwaogu, J. M., Yang, Y., Chan, A. P. C., & Chi, H. (2023). Application of drones in the architecture, engineering, and construction (AEC) industry. Automation in Construction, 150, 104827. https://doi.org/10.1016/j.autcon.2023.104827
  • Prasath Kumar, V. R., Balasubramanian, M., & Jagadish Raj, S. (2016). Robotics in Construction In-dustry. Indian Journal of Science and Technology, 9(23). https://doi.org/10.17485/ijst/2016/v9i23/95974
  • Rahimian, A., Hosseini, M. R., Martek, I., Taroun, A., Alvanchi, A., & Odeh, I. (2022). Predicting com-munication quality in construction projects: A fully-connected deep neural network approach. Automation in Construction, 139, 104268. https://doi.org/10.1016/j.autcon.2022.104268
  • Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin infor-mation systems. Data-Centric Engineering, 1. https://doi.org/10.1017/dce.2020.16
  • Sambasivan, M., & Soon, Y. W. (2007). Causes and effects of delays in Malaysian construction industry. International Journal of Project Management, 25(5), 517–526. https://doi.org/10.1016/j.ijproman.2006.11.007
  • Tariq, J., & Shujaa Safdar Gardezi, S. (2023). Study the delays and conflicts for construction projects and their mutual relationship: A review. Ain Shams Engineering Journal, 14(1), 101815. https://doi.org/10.1016/j.asej.2022.101815
  • Zhang, J., Liu, N., & Wang, S. (2021). Generative design and performance optimization of residential buildings based on parametric algorithm. Energy and Buildings, 244, 111033. https://doi.org/10.1016/j.enbuild.2021.111033
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp