Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

The building sector is significantly contributing to climate change, pollution, and energy crises, thus requiring a rapid shift to more sustainable construction practices. Here, we review the emerging practices of integrating renewable energies in the construction sector, with a focus on energy types, policies, innovations, and perspectives. The energy sources include solar, wind, geothermal, and biomass fuels. Case studies in Seattle, USA, and Manama, Bahrain, are presented. Perspectives comprise self-sufficiency, microgrids, carbon neutrality, intelligent buildings, cost reduction, energy storage, policy support, and market recognition. Incorporating wind energy into buildings can fulfill about 15% of a building's energy requirements, while solar energy integration can elevate the renewable contribution to 83%. Financial incentives, such as a 30% subsidy for the adoption of renewable technologies, augment the appeal of these innovations.

Building construction requires important amounts of freshwater, thus depleting the already stressed natural water resources. This issue could be addressed by using recycled water in construction and in building systems. However, integrating greywater recycling systems is limited by complexity, costs, vulnerability to environmental fluctuations, and coordination of policymakers, developers, and construction practitioners. Here, we review recycled water systems in buildings with focus on case studies of successful implementations, policies, recycled water treatment in buildings, and health aspects. Compared to conventional tap water, the incorporation of recycled water enhances the consistency and workability of reclaimed water concrete by 12–14%, and it increases concrete viscosity by 11% and yield stress by 25%. We discuss the intricacies of building water recycling systems, with emphasizing on conserving water, mitigating environmental impact, and enhancing economic efficiency. Challenges include water quality assurance, dual piping infrastructure, and regulatory compliance. Government interventions, including incentives, mandates, and subsidy policies, emerge as drivers for widespread adoption. Technological advancements, such as membrane filtration and advanced oxidation processes, are examined for strengths and limitations.

Dust generated in earthwork construction activities can seriously affect the air quality at a construction site and have adverse effects on the health of construction workers. To accurately and quantitively analyze the distribution characteristics of construction dust and the effect of dust prevention measures during earthwork construction under normal construction and construction with dust control measures, multiple collection points and one meteorological parameter collection point were placed at the construction site. From half an hour before the construction to half an hour after the construction, the particle concentration was recorded once every minute. The monitoring results indicated that there was a significant positive correlation between dust concentration during earthwork construction and the number of soil shipments. The dust concentration was highest at the earth excavation site, followed by the area of the waste truck’s transportation path. Earth excavation primarily resulted in the generation of many coarse particles, the concentration of which was the highest near the excavation site. The average concentration increments of PM_2.5 and TSP (total suspended particulate) caused by earthwork construction were 55.06 and 375.17 μg/m³ at the construction site, respectively. The concentration increment of PM_2.5 and TSP decreased by 72.01% and 40.16%, respectively, when a spray system and artificial sprinkling were adopted. Through the methodology and results of this study, construction companies can systemically plan their construction work by considering the key equipment to be used and can effectively manage the pollutants found within construction sites.

To help designers develop solutions to overcome the cracking problem in steel-plate-reinforced concrete composite shear walls due to the concrete shrinkage, the influence of steel plates and studs on the shrinkage behavior of high-performance concrete (HPC), including restrained shrinkage strain, shrinkage strain gradient, and cracking potential, were theoretically and experimentally investigated in this study. A model for theoretical analysis was used to research the shrinkage performance of concrete that was restrained by steel plates and studs. The major parameters involved in the experiments include the thickness and material elastic modulus of the steel plate, in addition to the diameter, height, and number of studs. It was found that the shrinkage of HPC decreases and its potential cracking increases with the increase of thickness and elastic modulus of the steel plate, and the diameter, height, and number of studs. The restraining effect of the steel plate and stud on the HPC shrinkage decreases with the distance of their respective locations. It demonstrates that the HPC near a steel plate and stud is prone to crack compared with that far away from the steel plate and stud. This potential could be reduced by uniformly restraining the HPC.

The prediction of the low-cycle fatigue life of bimetallic steel bars (BSBs) is essential to promote the engineering application of BSBs. However, research on the low-cycle fatigue properties of BSB is limited, and fatigue experiments are time-consuming. Moreover, considering that sufficient data are needed for model training, the lack of data hinders the leverage of typical data-driven machine learning, which is widely used in fatigue life prediction. To address this issue, a transfer learning framework was suggested to accurately predict the low-cycle fatigue life of BSBs with limited data. To achieve this goal, 54 data points obtained from low-cycle fatigue tests on BSBs and 264 data points of other metallic bars were collected. Source models based on artificial neural networks (ANNs) were first constructed using the collected source dataset. Then, the learned knowledge stored in the source models was transferred to the transfer models. After that, transfer models were further fine-tuned and then tested using the target dataset of BSBs. The ANN models, which were of the same structure as the transfer models but only trained with the target dataset without transferring deep features from the source models, were set as baseline models. Compared with baseline models, the constructed transfer models could be used to accurately predict the fatigue life of BSBs. Moreover, the influence of hidden layers of ANNs on accuracy was examined by comparing one-layer and two-layer transfer models. Furthermore, the influence of key parameters on fatigue life of metallic bars was evaluated by feature analysis.