Artificial intelligence (AI) and machine learning (ML) have become important aspects of daily life. However, beyond streamlining work through automated systems, these technologies also hold promise in reducing the environmental impact caused by construction projects.1 AI and ML enhance planning, scheduling, and facility management processes by enabling intelligent and sustainable construction practices.2
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In building interiors, AI and ML can be used to optimize energy and water usage by finding inefficiencies and suggesting necessary changes. Externally, AI and ML technologies can check air quality, noise levels, and waste disposal to quickly identify and remediate sources of pollution.1 This article explores the role of AI and ML in promoting sustainable construction and the associated challenges.
AI and ML in construction
Construction projects are complex and often suffer from miscalculations, inefficient resource allocation, setbacks due to poor planning, cost overruns, and safety hazards. Additionally, the building and construction sector accounts for a significant portion of global energy consumption and contributes 15% of CO.2 emissions.1 In view of growing environmental concerns, there is currently a shift towards intelligent and sustainable construction methods worldwide.2
AI and ML can significantly enhance the construction process and promote sustainability. While AI-based systems can perform tasks that require human intelligence, ML algorithms learn from data, recognize patterns, and decide on actions with minimal human involvement. AI and ML enable sustainability in all construction stages, from building design to operation.1
During the architectural planning phase, ML techniques can be used to accurately estimate project costs and complete construction in a sustainable manner with appropriate resource allocation.1 Use AI and ML to analyze massive datasets, predict project outcomes, minimize construction time, and automate repetitive tasks. Additionally, AI can assess the environmental footprint of construction materials and methods throughout their lifecycle, from raw material extraction to building demolition. This helps designers and builders make environmentally friendly choices.2
AI tools using environmental data can help you design energy-efficient and green buildings.2 Estimation of hourly energy consumption by ML helps in building operational decisions. Smart meters and individual load monitoring are another way to profile electrical equipment’s power usage through AI. Additionally, ML can improve the thermal experience of occupants by optimizing energy usage according to the solar conditions within the building.1
AI and ML technologies are critical to ensuring the health and well-being of residents by monitoring pollution sources and levels.2 Through real-time monitoring of air quality, noise levels, and waste management, AI systems quickly identify and address pollution issues, promoting a healthier environment. Additionally, AI-based air quality early warning systems and ML algorithms to estimate particulate matter concentrations can provide accurate insights and effectively reduce air pollution.1
ML models can also be used to assess the impact of city and country variables on waste treatment. The AI-based intelligent framework enables automatic recycling and enhances waste collection within the region.1 Real-time data analysis allows you to improve waste collection routes. This reduces fuel usage and reduces environmental pollution.2 Furthermore, by utilizing AI and ML, it will be possible to accurately measure and control noise pollution from construction sites. For example, ML can use field data to predict noise levels, and AI-based systems can identify irregularities in noise levels based on estimates. 1
AI-based frameworks can address multiple concerns in green buildings, including clean energy, sustainable biogas production, and solid waste management. Additionally, AI-integrated sensors for water pipelines can detect leaks and check water quality to provide residents with stable and clean water. Rapid analysis of building designs and construction plans using AI models can help automate the green building certification process and accelerate the adoption of such infrastructure. 2
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assignment
The integration of AI and ML technologies has enabled advances in intelligent and green architecture. However, these advances are not without challenges. The main challenge lies in the diversity and scale of the data that AI and ML algorithms rely on. Reconciling numerous disparate data sources to seamlessly integrate and coordinate them with ML algorithms is a major hurdle to getting the most out of these technologies.2
Data security and privacy are also important concerns, as construction projects contain sensitive information and any leakage could raise serious concerns. Additionally, continuous monitoring and investigation are essential for rapid decision-making, which requires high-end computing systems.2
The use of AI and ML also faces several ethical and social challenges. For example, the greater efficiency of these technologies in automating design and construction has increased concerns about job losses. Additionally, historical data used to train AI algorithms can contain biases. Therefore, scrutiny is needed to ensure that past discriminatory practices do not influence future project approvals, resource allocation, and urban planning.2
Overall, the full potential of AI and ML in sustainable construction can be harnessed by solving issues related to data privacy, talent training, and funding availability. Using AI and ML to enable smart, sustainable, human-centered cities requires collaboration with industry stakeholders, policymakers, and researchers with a strong legal framework.2
Latest development status
AI and ML are being used in innovative ways for sustainable construction. For example, recent research shows that of Asian Civil Engineering Journal evaluated the influence of nanosilica precursors on the compressive strength of mortar using advanced ML and proposed its use as a sustainable pozzolanic building material. Therefore, AI and ML can play a pivotal role in promoting the effective use of industrial waste as an alternative construction material.3
Another recent article environmental chemistry letter We considered the application of AI to waste management in smart cities. AI can be used in waste-to-energy systems, waste sorting robots, waste monitoring tools, smart bins, illegal dumping control, fossil and synthetic separation, resource recovery, and more. AI-based chemical analysis improves pyrolysis, carbon emissions estimation, and energy conversion. Moreover, AI-integrated waste identification and separation shows an accuracy of 72.8 to 99.95%.Four
Future prospects
As the use of AI and ML on construction sites increases, its potential must be harnessed to benefit society and the environment. AI advances 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 Environmentally responsible and socially responsible construction contributes to the circular economy and the well-being of current and future generations.2
The future of AI and ML in sustainable construction revolves around automation, optimization, and improved decision-making capabilities.1 Predictive analytics and real-time monitoring powered by AI and ML ensure not only the functionality and aesthetics of your infrastructure, but also its sustainability. Using AI and ML, we can predict a greener, smarter future for the construction industry and our planet.2
References and further information
1. KO Kazeem, TO Olawumi, T Osunsanmi (2023). The role of artificial intelligence and machine learning in strengthening construction processes and sustainable communities. building, 13(8), 2061. https://doi.org/10.3390/buildings13082061
2. Lane, New Jersey. (2023). Integrating cutting-edge artificial intelligence (AI), Internet of Things (IoT), and big data technologies into a smart and sustainable architecture, engineering, and construction (AEC) industry: Challenges and future directions. International Journal of Data Science and Big Data Analytics, 3(2), 73–95. https://doi.org/10.51483/ijdsbda.3.2.2023.73-95
3. Onyero, KC, Ebid, AM, Shadi Hanande. (2023). Effect of nanosilica precursors on compressive strength of mortar using advanced machine learning for sustainable buildings. Asian Civil Engineering Journal, twenty five(2), 1135-1148. https://doi.org/10.1007/s42107-023-00832-w
4. Huang, B., Yu, J., Chen, Z., Osman, A.I., Fargarhi, M., Ihara, I., Hamza, E.H., Rooney, D.W., and Yap, P.-S. (2023). ). Artificial intelligence for waste management in smart cities: A review. environmental chemistry letter, twenty one. https://doi.org/10.1007/s10311-023-01604-3
