A Review of the Application of Artificial Intelligence, Remote Sensing, and 3D Printing for the Sustainability of Civil Infrastructure
Publication: ASCE Inspire 2023
ABSTRACT
Resilience and sustainability of civil infrastructure have become major problems for about four decades. The concept of resilience and sustainability rose to notice in the late 1980s and has become a central issue worldwide. Most researchers, engineer practitioners, as well as construction industry are focused either on resilience or sustainability operating only without a mutual consideration of the findings, which leads to severe ineffective and inefficient sustainability of civil infrastructure. Though big data and the sustainability of civil infrastructure analysis are remaining unsolved and lagging, several engineering sectors have applied artificial intelligence (AI) to solve problems and analyze big data using machine learning (ML). However, the civil infrastructure industry is lagging to integrate AI, remote sensing (RS), and 3D printing (3DP) in the development of sustainability of civil infrastructure, and no developed process on how to desegregate these new applications. The main purpose of this research is to review these new applications of artificial intelligence, remote sensing, and 3D printing techniques for the sustainability of civil infrastructure. Results indicated that artificial intelligence, remote sensing, and 3D printing technology can increase the reliability and sustainability of civil infrastructure and the level of automation and standardization of civil construction engineering and pavement maintenance engineering leading to effective and efficient improvement in worker safety, reliability, and sustainability of construction materials, climate adaptability, and maintenance and rehabilitation repair accuracy and flexibility.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Davila Delgado, J. M., Bilal, M., Akinade, O. O., and Ahmed, A. (2021). “Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges.” Journal of Building Engineering, 44, 103299.
Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M. J., Ventura, S., Garrell, J. M., Otero, J., Romero, C., Bacardit, J., Rivas, V. M., Fernández, J. C., and Herrera, F. (2009). “KEEL: a software tool to assess evolutionary algorithms for data mining problems.” Soft computing, 13(3), 307–318.
Bocchini, P., Frangopol, D. M., Ummenhofer, T., and Zinke, T. (2014). “Resilience and Sustainability of Civil Infrastructure: Toward a Unified Approach.” Journal of Infrastructure Systems, 20(2), 04014004.
Borgeaud, M., Noll, J., and Bellini, A. (1994). “Multi-temporal comparisons of ERS-1 and JERS-1 SAR data for land applications.” Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS '94), vol, 3, 1603–1605.
Donepudi, P. K., Ahmed, A. A. A., and Saha, S. (2020). “Emerging market economy (EME) and artificial intelligence (AI): Consequences for the future of jobs.” PalArch’s J. Archaeol. Egypt/Egyptol, 17(6), 5562–5574.
Frangopol, D. M., and Liu, M. (2007). “Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost∗.” Structure and Infrastructure Engineering, 3(1), 29–41.
Garcia, J. M., and Camus, J. P. (2011). “Value creation in the resource business.” Journal of the Southern African Institute of Mining and Metallurgy, 111, 801–808.
Goldberg, D. E., and Holland, J. H. (1988). “Genetic algorithms and machine learning.” Machine Learning, 3(2-3), 95–99.
Gong, F., Cheng, X., Wang, Q., Chen, Y., You, Z., and Liu, Y. (2023). “A Review on the application of 3D Printing technology in pavement maintenance.” Sustainability, 15(7), 6237.
Griffiths, K., Boyle, C., and Henning, T. F. P. (2020). “Infrastructure sustainability rating tools – exploring opportunities for the engineering profession based on international research.” Australian Journal of Civil Engineering, 18(1), 18–28.
Grinter, R. E. (1995). “Using a configuration management tool to coordinate software development.” COCS '95 Proceedings of conference on organizational computing systems, ACM, Milpitas, California, USA, 168–177.
Jackson, R. J., Wojcik, A., and Miodownik, M. (2018). “3D printing of asphalt and its effect on mechanical properties.” Materials & Design, 160, 468–474.
Jensen, J. R., and Cowen, D. C. (1999). “Remote sensing of urban/suburban infrastructure and socio-economic attributes.” Photogrammetric Engineering and Remote Sensing, 65(5), 611–622.
Leeflang, P. S. H., Verhoef, P. C., Dahlström, P., and Freundt, T. (2014). “Challenges and solutions for marketing in a digital era.” European Management Journal, 32(1), 1–12.
Maktav, D., Erbek, F. S., and Jürgens, C. (2005). “Remote sensing of urban areas.” International Journal of Remote Sensing, 26(4), 655–659.
Manikandan, K., Wi, K., Zhang, X., Wang, K., and Qin, H. (2020). “Characterizing cement mixtures for concrete 3D printing.” Manufacturing Letters, 24, 33–37.
Manzoor, B., Othman, I., Durdyev, S., Ismail, S., and Wahab, M. H. (2021). “Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review.” Applied System Innovation, 4(3), 52.
Manzoor, B., Othman, I., Durdyev, S., Ismail, S., and Wahab, M. H. (2023). “Review of artificial intelligence applications for virtual sensing of underground utilities.” Sensors, 23(9), 4367.
Mhlanga, D. (2021). “Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies?” Sustainability, 13(11), 5788.
Mishra, D., Kumar, D., and Mishra, S. (2010). “Predictive data mining: Promising future application.” Int. J. of Computer and Communication Technology, 2(1), 20–28.
NRC (National Research Council). (2006). Geological and geotechnical engineering in the new millennium opportunities for research and technological innovation, The National Academic Press, Washington, D.C.
Nega, A., Nikraz, H., Herath, S., and Ghadimi, B. (2015). “Distress identification, cost analysis and pavement temperature prediction for the long-term pavement performance for Western Australia.” International Journal of Engineering and Technology, 7(4), 267–275.
Ngai, E. W. T., and Chan, E. W. C. (2005). “Evaluation of knowledge management tools using AHP.” Expert Systems with Applications, 29(4), 889–899.
Nilsson, N. J. (1969). “A mobile automation: An application of artificial intelligence techniques.” International Joint Conference on Artificial Intelligence, Stanford Research Institute, Washington, D.C., 1–26.
Olsen, M. J., Raugust, J. D., and Roe, G. V. (2013). “Use of advance geospatial data, tools, technologies, and information in Department of Transportation Projects.”, Transportation Research Board, Washington, D.C., 87.
Pieraccini, M. (2013). “Monitoring of Civil Infrastructures by Interferometric Radar: A Review.” The Scientific World Journal, 2013, 786961.
Salehi, H., and Burgueño, R. (2018). “Emerging artificial intelligence methods in structural engineering.” Engineering Structures, 171, 170–189.
Schnebele, E., Tanyu, B. F., Cervone, G., and Waters, N. (2015). “Review of remote sensing methodologies for pavement management and assessment.” European Transport Research Review, 7(2), 7.
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., and Guizani, M. (2019). “Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges.” IEEE Access, 7, 48572–48634.
Shirowzhan, S., Tan, W., and Sepasgozar, S. M. E. (2020). “Digital twin and cyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities.” ISPRS International Journal of Geo-Information, 9(4), 240.
Spencer, B. F., Hoskere, V., and Narazaki, Y. (2019). “Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring.” Engineering, 5(2), 199–222.
Stevenson, K. (2018). Repairing potholes with 3D Printers. Fabbaloo, Terran Data Corporation.
Tao, M., Mohammad, L., Nazzal, M., Zhang, Z., and Wu, Z. (2010). “Application of shakedown theory in characterizing traditional and recycled pavement base materials.” Journal of Transportation Engineering, 136(3), 214–222.
Tavana Amlashi, A., Alidoust, P., Pazhouhi, M., Pourrostami Niavol, K., Khabiri, S., and Ghanizadeh Ali, R. (2021). “AI-based formulation for mechanical and workability properties of eco-friendly concrete made by waste foundry sand.” Journal of Materials in Civil Engineering, 33(4), 04021038.
Ugwu, O. O., and Haupt, T. C. (2007). “Key performance indicators and assessment methods for infrastructure sustainability—a South African construction industry perspective.” Building and Environment, 42(2), 665–680.
Wu, X., Zhu, X., Wu, G., and Ding, W. (2014). “Data mining with big data.” IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.
Yossef, M., and Chen, A. (2015). “Applicability and Limitations of 3D Printing for Civil Structures.” Proceedings of the 2015 Conference on Autonomous and Robotic Construction of InfrastructureUSA: Ames, 237–246.
Information & Authors
Information
Published In
ASCE Inspire 2023
Pages: 123 - 132
History
Published online: Nov 14, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Big data
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction industry
- Construction management
- Construction materials
- Engineering fundamentals
- Engineering materials (by type)
- Infrastructure
- Infrastructure construction
- Infrastructure resilience
- Materials engineering
- Measurement (by type)
- Methodology (by type)
- Practice and Profession
- Research methods (by type)
- Sensors and sensing
- Sustainable development
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.