Unveiling the Potential of AI Assistants:  A Review of AI in Building Materials Selection

Andi Prasetiyo Wibowo

doi:10.24002/jarina.v3i2.9293

Authors

Andi Prasetiyo Wibowo Coventry University

DOI:

https://doi.org/10.24002/jarina.v3i2.9293

Keywords:

Artificial intelligence , Building Materials, Construction materials, Efficiency, Estimation

Abstract

Fast-advancing Artificial Intelligence (AI) has transformed many industries, including construction. AI offers innovative solutions to increase efficiency and effectiveness in various aspects of construction, one of which is selecting building materials. By reading relevant literature, this study aims to determine how much AI can help choose building materials so that projects go more easily and quickly. Using SCOPUS as its principal database, this study conducted a literature review. The method of this study begins with the process of filtering articles using the key string: ("artificial intelligence" OR AI) AND ("building materials" OR "construction materials") AND ("efficiency" OR "time" OR "cost") to find relevant articles. The research results show that AI can help improve time and cost efficiency in selecting building materials through various means, such as data analysis, material recommendations, cost optimisation, and performance estimation. In conclusion, this study shows that AI has much potential to make choosing building materials more efficient and effective, thus reducing building time, costs, and environmental damage. Still, it also dramatically impacts building monitoring and maintenance and task automation.

References

Q. L. Yu, P. Spiesz, and H. J. H. Brouwers, “Ultra-lightweight concrete: Conceptual design and performance evaluation,” Cem. Concr. Compos., vol. 61, pp. 18–28, Aug. 2015, doi: 10.1016/j.cemconcomp.2015.04.012.

L. Mohammed, M. N. M. Ansari, G. Pua, M. Jawaid, and M. S. Islam, “A Review on Natural Fiber Reinforced Polymer Composite and Its Applications,” Int. J. Polym. Sci., vol. 2015, pp. 1–15, 2015, doi: 10.1155/2015/243947.

T. Mahendarto, “From Artificial Intelligence to Artificial Consciousness: An Interior Design Implication,” J. Artif. Intell. Archit., vol. 2, no. 1, pp. 41–52, Feb. 2023, doi: 10.24002/jarina.v2i1.6627.

Z. Xu, T. Song, S. Guo, J. Peng, L. Zeng, and M. Zhu, “Robotics technologies aided for 3D printing in construction: a review,” Int. J. Adv. Manuf. Technol., vol. 118, no. 11–12, pp. 3559–3574, Feb. 2022, doi: 10.1007/s00170-021-08067-2.

Z. Wu, “Performance analysis of new energy-saving and environment-friendly materials for building decoration based on the whole life cycle,” J. Exp. Nanosci., vol. 18, no. 1, p. 2170358, Dec. 2023, doi: 10.1080/17458080.2023.2170358.

S. O. Abioye et al., “Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges,” J. Build. Eng., vol. 44, p. 103299, Dec. 2021, doi: 10.1016/j.jobe.2021.103299.

Y. Pan and L. Zhang, “Roles of artificial intelligence in construction engineering and management: A critical review and future trends,” Autom. Constr., vol. 122, p. 103517, Feb. 2021, doi: 10.1016/j.autcon.2020.103517.

M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” Int. J. Surg., vol. 88, p. 105906, Apr. 2021, doi: 10.1016/j.ijsu.2021.105906.

J. C. Perkowski, “Technical trends in the E and C business: The next 10 years,” J. Constr. Eng. Manag., vol. 114, no. 4, pp. 565–576, 1988, doi: 10.1061/(ASCE)0733-9364(1988)114:4(565).

K. Srivilas and P. Cherntanomwong, “Routing algorithm in warehouse with congestion consideration using an ACO with VLC support,” presented at the 2017 International Electrical Engineering Congress, iEECON 2017, 2017. doi: 10.1109/IEECON.2017.8075830.

W.-B. Zhu et al., “High-Performance Fiber-Film Hybrid-Structured Wearable Strain Sensor from a Highly Robust and Conductive Carbonized Bamboo Aerogel,” ACS Appl. Bio Mater., vol. 3, no. 12, pp. 8748–8756, 2020, doi: 10.1021/acsabm.0c01128.

M. Taxiarchou, D. Panias, C. Panagiotopoulou, A. Karalis, and C. Dedelousdis, “Study on the suitability of volcanic amorphous aluminosilicate rocks (perlite) for the synthesis of geopolymer-based concrete,” presented at the ASTM Special Technical Publication, 2013, pp. 34–53. doi: 10.1520/STP156620120077.

D. Kumar, M. Kamran, C. Zhang, L. Wang, H. Zhai, and S. Demirkesen, “Evaluating the Impact of Contracting and Procurement Methods on Energy and Carbon Emissions Reduction in the Public Construction Sector,” presented at the ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World - Selected Papers from ASCE Inspire 2023, 2023, pp. 886–894. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179855022&partnerID=40&md5=ebc1cedf0fd7d0a1e15af26e53c2df1b

V. Arnold, M. Ashkerov, and S. B. Globa, “Methods for Automatic Detection of Asbestos in Construction Materials,” presented at the Lecture Notes in Networks and Systems, 2021, pp. 1355–1361. doi: 10.1007/978-3-030-69415-9_149.

F. Aldebei and M. Dombi, “Mining the built environment: Telling the story of urban mining,” Buildings, vol. 11, no. 9, 2021, doi: 10.3390/buildings11090388.

J. A. Becerra-Duitama and D. Rojas-Avellaneda, “Pozzolans: A review,” Eng. Appl. Sci. Res., vol. 49, no. 4, pp. 495–504, 2022, doi: 10.14456/easr.2022.49.

B. Chen, X. Wang, D. Sun, and X. Xie, “Integrated system of structural health monitoring and intelligent management for a cable-stayed bridge,” Sci. World J., vol. 2014, 2014, doi: 10.1155/2014/689471.

G. Hong, X.-P. Wang, J. Lee, Y.-C. Loo, and M. Blumenstein, “Modelling long-term bridge deterioration at structural member level using Artificial Intelligence techniques,” presented at the Applied Mechanics and Materials, 2011, pp. 444–453. doi: 10.4028/www.scientific.net/AMM.99-100.444.

Y. E. Vasiliev, M. A. Fineeva, E. V. Kotlyarskiy, and I. Y. Sarichev, “Intellectualization of the Monitoring Process of Street and Road Network Objects,” presented at the 2021 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex, TIRVED 2021 - Conference Proceedings, 2021. doi: 10.1109/TIRVED53476.2021.9639133.

J.-L. Wei, G.-J. Zhang, M. Dong, H.-B. Mu, and Z. Yan, “Research progress on insulation condition estimation for oil-immersed power transformer,” presented at the Proceedings of the International Symposium on Electrical Insulating Materials, 2008, pp. 32–35. doi: 10.1109/ISEIM.2008.4664436.

T. Ishtiak, S. Ahmed, M. H. Anila, S. Islam, R. Shelim, and T. Farah, “Road state classification of Bangladesh with convolutional neural network approach,” presented at the WMSCI 2019 - 23rd World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings, 2019, pp. 129–134. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074160745&partnerID=40&md5=6168be04b3232bbf06462de9f909f14e

C. Scuro, R. S. Olivito, F. Lamonaca, and D. L. Carnì, “Structural Health Monitoring Based on Artificial Intelligence Algorithm and Acoustic Emission Analysis,” presented at the Lecture Notes in Civil Engineering, 2021, pp. 258–266. doi: 10.1007/978-3-030-64908-1_24.

E. Sadrossadat, H. Basarir, A. Karrech, R. Durham, A. Fourie, and H. Bin, “The optimization of cemented hydraulic backfill mixture design parameters for different strength conditions using artificial intelligence algorithms,” presented at the Springer Series in Geomechanics and Geoengineering, 2020, pp. 219–227. doi: 10.1007/978-3-030-33954-8_28.

P. R. Roberge, “Tools for corrosion intelligence,” presented at the Applications of Artificial Intelligence in Engineering, 1994, pp. 407–414. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-0028553388&partnerID=40&md5=197294974b74b552ce0aeffe221cb741

T. Honda, T. Tanaka, S. Doi, S. Uchida, and M. Q. Feng, “Visualization of voids between tile and concrete by multi-layered scanning method with electromagnetic waves,” J. Robot. Mechatron., vol. 31, no. 6, pp. 863–870, 2019, doi: 10.20965/jrm.2019.p0863.

R. Ortiz and P. Ortiz, “Vulnerability Index: A New Approach for Preventive Conservation of Monuments,” Int. J. Archit. Herit., vol. 10, no. 8, pp. 1078–1100, 2016, doi: 10.1080/15583058.2016.1186758.

H. Abaza, A. Clark, A. Schwartz, H. J. Ducre, and D. A. Guerra-Zubiaga, “INDUSTRIALIZING RESIDENTIAL CONSTRUCTION USING ARTIFICIAL INTELLIGENT (AI) ROBOTICS,” presented at the ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), 2022. doi: 10.1115/IMECE2022-96675.

Z. Shaikh, T. Naaz, and B. Rajput, “Intelligent Flooring Systems in Interiors-Exploring the Impact on Well-Being,” presented at the Lecture Notes in Mechanical Engineering, 2022, pp. 361–375. doi: 10.1007/978-981-19-0296-3_33.

S.-S. Leu, P. V. Hong Son, and P. T. Hong Nhung, “Optimize negotiation price in construction procurement using Bayesian Fuzzy Game Model,” KSCE J. Civ. Eng., vol. 19, no. 6, pp. 1566–1572, 2015, doi: 10.1007/s12205-014-0522-2.

K.-S. Liu and M.-H. Lin, “Performance assessment on the application of artificial intelligence to sustainable supply chain management in the construction material industry,” Sustain. Switz., vol. 13, no. 22, 2021, doi: 10.3390/su132212767.

T. Li, E. Zhu, Z. Bai, W. Cai, H. Jian, and H. Liu, “Predicting and assessing greenhouse gas emissions during the construction of monorail systems using artificial intelligence,” Environ. Sci. Pollut. Res., vol. 31, no. 8, pp. 12229–12244, 2024, doi: 10.1007/s11356-023-31783-z.

S. Geng, Q. Luo, K. Liu, Y. Li, Y. Hou, and W. Long, “Research status and prospect of machine learning in construction 3D printing,” Case Stud. Constr. Mater., vol. 18, 2023, doi: 10.1016/j.cscm.2023.e01952.

I. Muhammad, K. Ying, M. Nithish, J. Xin, Z. Xinge, and C. C. Cheah, “Robot-Assisted Object Detection for Construction Automation: Data and Information-Driven Approach,” IEEEASME Trans. Mechatron., vol. 26, no. 6, pp. 2845–2856, 2021, doi: 10.1109/TMECH.2021.3100306.

N. D. Cortiços, “Self-learning and self-repairing technologies to establish autonomous building maintenance,” presented at the MATEC Web of Conferences, 2019. doi: 10.1051/matecconf/201927804004.

U. J. Pont, N. Ghiassi, S. Fenz, J. Heurix, and A. Mahdavi, “SEMERGY: Application of semantic web technologies in performance-guided building design optimization,” J. Inf. Technol. Constr., vol. 20, pp. 107–120, 2015.

D. Blom, I. Berchenko, F. Samie, and D. Frajberg, “Shell Autonomous Integrity Recognition - Machine Vision Application for Inspections,” presented at the Society of Petroleum Engineers - ADIPEC 2022, 2022. doi: 10.2118/211838-MS.

P. Hirsch, A. Palfi, and M. Gronalt, “Solving a time constrained two-crane routing problem for material handling with an ant colony optimisation approach: An application in the roof-tile industry,” Int. J. Prod. Res., vol. 50, no. 20, pp. 6005–6021, 2012, doi: 10.1080/00207543.2011.640957.

C.-C. Wei, C.-C. Hsiang, and T.-C. Shan, “Decision support model with life cycle assessment for building in design phase,” presented at the 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings, 2014, pp. 966–971. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84912564129&partnerID=40&md5=57061366d79d5545d922ed1484d0e0b2

S. S. Sahota, H. C. Arora, A. Kumar, K. Kumar, and H. S. Rai, “ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers,” presented at the Lecture Notes in Networks and Systems, 2023, pp. 231–244. doi: 10.1007/978-3-031-31153-6_20.

M. Anjum, K. Khan, W. Ahmad, A. Ahmad, M. N. Amin, and A. Nafees, “New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete,” Materials, vol. 15, no. 18, 2022, doi: 10.3390/ma15186261.

H. N. Muliauwan, D. Prayogo, G. Gaby, and K. Harsono, “Prediction of Concrete Compressive Strength Using Artificial Intelligence Methods,” presented at the Journal of Physics: Conference Series, 2020. doi: 10.1088/1742-6596/1625/1/012018.

V. Nagarkar, P. Kulkarni, and S. Londhe, “Prediction of Concrete Compressive Strength Using Fuzzy Logic and Model Tree,” in Lecture Notes in Civil Engineering, vol. 74, 2020, pp. 231–240. doi: 10.1007/978-981-15-4079-0_20.

V. K. Bhadke, “Prediction of Concrete Strength with Artificial Neural Networks Enhanced by Nano-Silica,” presented at the 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, 2023, pp. 959–963. doi: 10.1109/UPCON59197.2023.10434809.

M. Mohtasham Moein et al., “Predictive models for concrete properties using machine learning and deep learning approaches: A review,” J. Build. Eng., vol. 63, 2023, doi: 10.1016/j.jobe.2022.105444.

R. Kazemi and M. Z. Naser, “Towards sustainable use of foundry by-products: Evaluating the compressive strength of green concrete containing waste foundry sand using hybrid biogeography-based optimization with artificial neural networks,” J. Build. Eng., vol. 76, 2023, doi: 10.1016/j.jobe.2023.107252.

M. N. Amin, W. Ahmad, K. Khan, A. Ahmad, S. Nazar, and A. A. Alabdullah, “Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions,” Materials, vol. 15, no. 15, 2022, doi: 10.3390/ma15155207.

X. Pan et al., “Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients,” Materials, vol. 15, no. 12, 2022, doi: 10.3390/ma15124194.

A. Tavana Amlashi, P. Alidoust, M. Pazhouhi, K. Pourrostami Niavol, S. Khabiri, and A. R. Ghanizadeh, “AI-based formulation for mechanical and workability properties of eco-friendly concrete made by waste foundry sand,” J. Mater. Civ. Eng., vol. 33, no. 4, 2021, doi: 10.1061/(ASCE)MT.1943-5533.0003645.

Z. Gao, X. Fan, T. Xia, W. Xue, and S. Gao, “Research on TTT Curve of Alloy structural Steel based on Machine Learning,” presented at the Journal of Physics: Conference Series, 2023. doi: 10.1088/1742-6596/2459/1/012139.

D. Li, P. Wang, N. Sarhan, and M. Sharaf, “Soft computational approach on the thermoelastic assessment of the sandwich beams with transversely functionally graded composite concrete face-sheets as an advanced building material,” Mech. Adv. Mater. Struct., 2023, doi: 10.1080/15376494.2023.2227413.

D. A. Joshi and M. Shewale, “Structural Engineering: Trends, Applications, and Advances,” in Data Science for Civil Engineering: A Beginner’s Guide, 2023, pp. 89–123. doi: 10.1201/9781003316671-4.

M. Ji, W. Zhang, X. Diao, G. Wang, and H. Miao, “Intelligent Automation Manufacturing for Betula Solid Timber Based on Machine Vision Detection and Optimization Grading System Applied to Building Materials,” Forests, vol. 14, no. 7, 2023, doi: 10.3390/f14071510.

F. Zhu, X. Wu, M. Zhou, M. M. S. Sabri, and J. Huang, “Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design,” Materials, vol. 15, no. 11, 2022, doi: 10.3390/ma15113833.

A. Ginzburg, O. Kuzina, and A. Ryzhkova, “Unified resources marking system as a way to develop artificial intelligence in construction,” presented at the IOP Conference Series: Materials Science and Engineering, 2018. doi: 10.1088/1757-899X/365/6/062021.

Y. Li, K. Ding, L. Wang, W. Zheng, Z. Peng, and S. Guo, “An optimizing model for solving outsourcing supplier selecting problem based on particle swarm algorithm,” J. Ind. Prod. Eng., vol. 35, no. 8, pp. 526–534, 2018, doi: 10.1080/21681015.2018.1533893.

T.-H. Fu and W.-D. Yu, “A pilot study of A+C method for reducing GHG emissions in construction industry,” presented at the 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, AIMSEC 2011 - Proceedings, 2011, pp. 6928–6931. doi: 10.1109/AIMSEC.2011.6010909.

G. Banias, C. Achillas, C. Vlachokostas, N. Moussiopoulos, and I. Papaioannou, “A web-based Decision Support System for the optimal management of construction and demolition waste,” Waste Manag., vol. 31, no. 12, pp. 2497–2502, 2011, doi: 10.1016/j.wasman.2011.07.018.

N. Tsydenova, T. Becker, and G. Walther, “Optimised design of concrete recycling networks: The case of North Rhine-Westphalia,” Waste Manag., vol. 135, pp. 309–317, 2021, doi: 10.1016/j.wasman.2021.09.013.

C. Völker, S. Kruschwitz, B. M. Torres, R. Firdous, G. A. Zia, and D. Stephan, “ACCELERATING THE SEARCH FOR ALKALI-ACTIVATED CEMENTS WITH SEQUENTIAL LEARNING,” presented at the fib Symposium, 2022, pp. 153–161. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143896280&partnerID=40&md5=142862124eecec3f2a96bef49b4a7176

D. Yang et al., “Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence,” Materials, vol. 15, no. 12, 2022, doi: 10.3390/ma15124311.

S. İpek, G. Özer Yaman, and C. Kılınç, “Investigation of thermal conductivity of rubberized concrete as an energy-efficient building material and modeling by artificial intelligence,” Arch. Civ. Mech. Eng., vol. 23, no. 3, 2023, doi: 10.1007/s43452-023-00701-y.

D. Bajno, A. Grzybowska, and Ł. Bednarz, “Old and modern wooden buildings in the context of sustainable development,” Energies, vol. 14, no. 18, 2021, doi: 10.3390/en14185975.

M. R. Sabour, G. A. Dezvareh, and K. P. Niavol, “Application of Artificial Intelligence Methods in Modeling Corrosion of Cement and Sulfur Concrete in Sewer Systems,” Environ. Process., vol. 8, no. 4, pp. 1601–1618, 2021, doi: 10.1007/s40710-021-00542-y.

Z. Wei, Y. Zandi, M. Gholizadeh, A. Selmi, A. Roco-Videla, and U. Konbr, “On the optimization of building energy, material, and economic management using soft computing,” Adv. Concr. Constr., vol. 11, no. 6, pp. 455–468, 2021, doi: 10.12989/acc.2021.11.6.455.

K. W. Mui, L. T. Wong, M. K. Satheesan, and A. Balachandran, “A hybrid simulation model to predict the cooling energy consumption for residential housing in Hong Kong,” Energies, vol. 14, no. 16, 2021, doi: 10.3390/en14164850.

X. Wang, X. Lü, L. Vähä-Savo, and K. Haneda, “A Novel AI-Based Thermal Conductivity Predictor in the Insulation Performance Analysis of Signal-Transmissive Wall,” Energies, vol. 16, no. 10, 2023, doi: 10.3390/en16104211.

T. Rymarczyk, G. Kłosowski, and E. Kozłowski, “A non-destructive system based on electrical tomography and machine learning to analyze the moisture of buildings,” Sens. Switz., vol. 18, no. 7, 2018, doi: 10.3390/s18072285.

C.-X. Yu et al., “Application of Deep Learning Techniques for Thermal Imagery Analysis in Abnormal Identification of Floor Tiles in Heritage Environments,” presented at the 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, 2023, pp. 1878–1884. doi: 10.1109/APSIPAASC58517.2023.10317400.

M. Marzouk, M. ElSharkawy, P. Elsayed, and A. Eissa, “Resolving deterioration of heritage building elements using an expert system,” Int. J. Build. Pathol. Adapt., vol. 38, no. 5, pp. 721–735, 2020, doi: 10.1108/IJBPA-12-2019-0106.

M. M. Marzouk, M. S. Abdelhamid, and M. T. Elsheikh, “Selecting building materials using system dynamics and ant colony optimization,” presented at the ICSDC 2011: Integrating Sustainability Practices in the Construction Industry - Proceedings of the International Conference on Sustainable Design and Construction 2011, 2012, pp. 577–584. doi: 10.1061/41204(426)71.

M. Marzouk, M. Abdelhamid, and M. Elsheikh, “Selecting sustainable building materials using system dynamics and ant colony optimization,” J. Environ. Eng. Landsc. Manag., vol. 21, no. 4, pp. 237–247, 2013, doi: 10.3846/16486897.2013.788506.

B. G. D. DeSoto and B. T. Adey, “Preliminary Resource-based Estimates Combining Artificial Intelligence Approaches and Traditional Techniques,” presented at the Procedia Engineering, 2016, pp. 261–268. doi: 10.1016/j.proeng.2016.11.618.

E. K. Zavadskas, R. Bausys, B. Juodagalviene, and I. Garnyte-Sapranaviciene, “Model for residential house element and material selection by neutrosophic MULTIMOORA method,” Eng. Appl. Artif. Intell., vol. 64, pp. 315–324, 2017, doi: 10.1016/j.engappai.2017.06.020.

A. N. Kaulage, P. K. Tomar, R. Pant, L. Debnath, R. K. Ibrahim, and M. B. Alazzam, “Revolutionizing Material Selection in Construction 4.0: A Comparative Analysis of Artificial Intelligence Technologies,” presented at the 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023, 2023, pp. 2707–2712. doi: 10.1109/ICACITE57410.2023.10182419.

A. Kanyilmaz, M. Birhane, R. Fishwick, and C. del Castillo, “Reuse of Steel in the Construction Industry: Challenges and Opportunities,” Int. J. Steel Struct., vol. 23, no. 5, pp. 1399–1416, 2023, doi: 10.1007/s13296-023-00778-4.

A. Rashidi, M. H. Sigari, M. Maghiar, and D. Citrin, “An analogy between various machine-learning techniques for detecting construction materials in digital images,” KSCE J. Civ. Eng., vol. 20, no. 4, pp. 1178–1188, 2016, doi: 10.1007/s12205-015-0726-0.