Editorial Feature

Harnessing AI and Machine Learning for Sustainable Construction

Artificial intelligence (AI) and machine learning (ML) have become a key aspect of everyday life. However, beyond streamlining tasks with automated systems, these technologies also hold promise when it comes to mitigating environmental impacts caused by construction projects.1 By empowering intelligent and sustainable construction practices, AI and ML enhance planning, scheduling, and facility management processes.2

Harnessing AI and ML for Sustainable Construction

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In building interiors, AI and ML can be used to optimize energy and water usage by finding inefficiencies and suggesting required changes. Externally, AI and ML technologies can check air quality, noise levels, and waste disposal to rapidly identify and rectify pollution sources.1 This article explores the role of AI and ML in promoting sustainable construction and the related challenges.

AI and ML in Construction

Construction projects are fraught with complexities, often plagued by setbacks, cost overruns, and safety hazards stemming from miscalculations, inefficient resource allocation, and inadequate planning. Additionally, the building and construction sector accounts for a significant portion of global energy consumption, contributing to 15 % of CO2 emissions.1 In light of escalating environmental apprehensions, there is now a worldwide pivot towards intelligent and sustainable construction methodologies.2

AI and ML can significantly enhance the construction process and promote sustainability. While AI-based systems can perform tasks that otherwise need human intelligence, ML algorithms learn from data, recognize patterns, and decide actions with the least human involvement. Sustainability in all the construction phases, from design to operation of buildings, is possible through AI and ML.1

In the building planning stage, ML methods can be employed to estimate project costs correctly and accomplish construction sustainably with proper resource allocation.1 Vast datasets can be analyzed using AI and ML to forecast project outcomes, minimize construction times, and automate recurring tasks. Moreover, the environmental footprint of construction materials and methods across their entire life cycle (from raw material extraction to building demolition) can be evaluated with AI. This helps designers and builders make eco-friendly choices.2

Energy-efficient and eco-friendly buildings can be designed by AI tools using environmental data.2 Hourly estimates of the energy consumption through ML can assist in the operational decisions of a building. Smart metering and discrete load monitoring are other ways to profile electric appliance power usage through AI. Additionally, ML can improve the occupant’s thermal experience by optimizing energy usage according to daylight conditions in a building.1

AI and ML techniques are pivotal in ensuring the health and well-being of occupants by monitoring pollution sources and levels.2 Through real-time monitoring of air quality, noise levels, and waste management, AI systems can swiftly identify and address pollution issues, fostering a healthier environment. Additionally, AI-based early warning systems for air quality and ML algorithms for estimating particulate matter concentrations can provide accurate insights, effectively mitigating air pollution.1

ML models can also be used to evaluate the impact of city and national variables on waste disposal. AI-based intelligent frameworks enable automated recycling and enhance waste collection in a region.1 Waste collection routes can be improved through real-time data analysis. This decreases fuel usage and lowers environmental pollution.2 Furthermore, accurate measuring and control of noise pollution from construction sites is possible using AI and ML. For example, ML can forecast noise levels using data from a site, and then AI-based systems can identify noise level irregularities based on estimates. 1

AI-based frameworks can address multiple concerns in green buildings like clean energy, sustainable biogas production, and solid waste management. Moreover, AI-integrated sensors for water pipelines can detect leaks and check water quality to provide residents with a steady and clean water supply. A quick analysis of building designs and construction plans using AI models helps automate the green building certification process and encourages the adoption of such infrastructure. 2

For More on Sustainability: Advancing Sustainable Construction with Mycelium-Based Solutions


The integration of AI and ML technologies has enabled the advancement of intelligent and environmentally conscious construction. Yet, these advancements are not without their challenges. A primary challenge lies in the sheer diversity and scale of the data that AI and ML algorithms rely on. Harmonizing the multitude of heterogeneous data sources to seamlessly integrate and align with ML algorithms poses a significant obstacle in leveraging these technologies to their fullest potential.2

Data security and privacy is another important concern as construction projects encompass sensitive information, and any leak can raise severe concerns. Moreover, continuous monitoring and investigation are critical for quick decision-making, which requires high-end computational systems.2

The use of AI and ML also faces some ethical and social challenges. For instance, the high efficiency of these technologies in automated designing and construction increases apprehensions regarding job displacement. Additionally, the historical data used to train AI algorithms may contain biases. Thus, scrutiny is required to ensure that past discriminatory practices do not impact future project approvals, resource allocation, or urban planning.2

Overall, the maximum potential of AI and ML in sustainable construction can be harnessed by resolving the issues related to data privacy, personnel training, and funds availability. Collaboration between industry stakeholders, policymakers, and researchers with robust legal frameworks is crucial to realize smart, sustainable, and human-centric cities using AI and ML.2

Latest Developments

AI and ML are being used in innovative ways for sustainable construction. For example, a recent study in the Asian Journal of Civil Engineering used advanced ML to evaluate the impact of nano-silica precursor on the compressive strength of mortar and proposed its use as a sustainable pozzolanic building material. Thus, AI and ML can play a pivotal role in facilitating the effective utilization of industrial waste as alternative construction materials.3

Another recent article in Environmental Chemistry Letters reviewed the application of AI for waste management in smart cities. AI can be employed in waste-to-energy systems, waste-sorting robots, waste monitoring tools, smart bins, regulating illegal dumping, sorting fossils and synthetic materials, and resource recovery. AI-based chemical analysis improves pyrolysis, carbon emission estimation, and energy conversion. Additionally, AI-integrated identification and sorting of waste exhibit 72.8 to 99.95 % accuracy.4

Future Prospects

With the rising use of AI and ML in construction, it is necessary to channel its potential for benefitting society and the environment. AI advances the UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 11 (Sustainable Cities and Communities) by enabling energy efficiency and sustainable infrastructure construction.1,2 An environmentally conscious and socially responsible construction will contribute to a circular economy and the well-being of present as well as future generations.2

The future of AI and ML in sustainable construction revolves around improved automation, optimization, and decision-making abilities.1 Predictive analytics and real-time monitoring through AI and ML not only ensure the functionality and pleasing aesthetics of infrastructure but also its sustainability. A greener and smarter future for the construction sector and the Earth is foreseeable using AI and ML.2

References and Further Reading

1. Kazeem, K. O., Olawumi, T. O., & Osunsanmi, T. (2023). Roles of Artificial Intelligence and Machine Learning in Enhancing Construction Processes and Sustainable Communities. Buildings13(8), 2061. https://doi.org/10.3390/buildings13082061

2. Rane, N. L.. (2023). Integrating Leading-Edge Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies for Smart and Sustainable Architecture, Engineering and Construction (AEC) Industry: Challenges and Future Directions. International Journal of Data Science and Big Data Analytics3(2), 73–95. https://doi.org/10.51483/ijdsbda.3.2.2023.73-95

3. Onyelowe, K. C., Ebid, A. M., & Shadi Hanandeh. (2023). The influence of nano-silica precursor on the compressive strength of mortar using Advanced Machine Learning for sustainable buildings. Asian Journal of Civil Engineering25(2), 1135–1148. https://doi.org/10.1007/s42107-023-00832-w

4. Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P.-S. (2023). Artificial intelligence for waste management in smart cities: a review. Environmental Chemistry Letters21. https://doi.org/10.1007/s10311-023-01604-3

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Nidhi Dhull

Written by

Nidhi Dhull

Nidhi Dhull is a freelance scientific writer, editor, and reviewer with a PhD in Physics. Nidhi has an extensive research experience in material sciences. Her research has been mainly focused on biosensing applications of thin films. During her Ph.D., she developed a noninvasive immunosensor for cortisol hormone and a paper-based biosensor for E. coli bacteria. Her works have been published in reputed journals of publishers like Elsevier and Taylor & Francis. She has also made a significant contribution to some pending patents.  


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