Artificial intelligence in green building

Debrah, Caleb; Chan, Albert P.C.; Darko, Amos

doi:10.1016/j.autcon.2022.104192

Cited by 115 publications

(68 citation statements)

References 120 publications

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“…Cyber-physical technologies, such as the IoT and cloud computing, which offer high-performance computing and well-networked connectivity, open opportunities for the efficient use of energies by establishing smart building systems. Sensors plus wireless further improve the sensitivity to temperature, thermal comfort, humidity, and the danger of buildings in operation management, helping achieve automatic building maintenance [26,27]. Energy-saving and environmental protection materials, such as polymer nanocomposites, are indispensable for sustainable building operation.…”

Section: Maintaining Low Energy and Resource Consumptionmentioning

confidence: 99%

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Wang

Zhang

2022

Buildings

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Advances in technology provides the potential to innovate sustainability in the building construction industry. Drawing on the literature and expert reviews, this research discloses the potential of 14 specific technologies in waste minimisation, energy saving, and efficiency improvement for sustainable building construction and develops a conceptual framework engaging the building life cycle (the planning, construction, use and operation, and demolition phases) and the actors for assessing technological innovation at a local level. This framework is used to identify how technologies were innovated for sustainable building construction through the selected 3017 patent invention applications from the Yangtze River Delta (YRD), one of the fastest urbanising areas and the largest market for the building industry in China. Findings unveiled that innovation is uneven amongst the 14 technologies and the degree of mixing was relatively low in the YRD. The contribution of the technological innovation to sustainability is mainly actualised through efficiency improvements in building construction (2265) and through directly reducing waste (1094) and energy consumption (642). Some general-purpose technologies (e.g., blockchain, cloud computing), which assume to fundamentally innovate the industry, are mostly absent with less than 10 records each, leaving the potential for future adaptive technological innovation. Furthermore, state-owned enterprises as the main sources of patent inventions amongst applicants in the YRD may suggest the dissimilar path of China towards technology innovation compared to its Anglo-American counterparts.

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Section: Maintaining Low Energy and Resource Consumptionmentioning

confidence: 99%

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Wang

Zhang

2022

Buildings

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“…Benefit − basedmetrics :x i = y i − min y i max y i − min y i (15) Cost − basedmetrics :x i = max y i − y i max y i − min y i (16) In summary, it is obvious that the larger δ i is, the better the design solution is, and it can be concluded that the optimal solution in Table 6…”

Section: Example Introductionmentioning

confidence: 99%

“…Junqing Li 14 proposed the use of genetic algorithm-based BIM models to explore building energy efficiency methods. Caleb Debrah 15 proposed the application of artificial intelligence in the field of green building to improve the sustainability of the construction industry. Y Xiang 16 used a multi-objective particle swarm optimization algorithm to solve a multi-objective equilibrium optimization model for schedule, quality, safety and environment of engineering projects.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective integrated optimization study of Prefabricated building projects introducing sustainable levels

Peng

Feng

Zhang

et al. 2022

Preprint

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As construction becomes greener, people have higher and higher requirements for engineering project management, which makes it necessary to deeply study the comprehensive optimization of schedule, cost and sustainability level. Adhering to the concept of low carbon and green, the article takes carbon emission factor into the total cost of building construction and improves the traditional cost objective of engineering projects; then quantitatively analyzes the economic, environmental and social impacts of assembled buildings from the perspective of sustainability, and introduces the sustainability objective into the traditional duration-cost problem study, taking the duration of each job in the double code arrow diagram as the independent variable to construct the duration -cost-sustainability level multi-objective optimization model. In order to solve the type effectively, a series of Pareto optimal solutions are obtained using the NSGA-II algorithm, and the efficacy coefficient method is used for program decision making. The results show that the Pareto solution set can provide effective support for the project manager’s decision making, and the NSGA-II algorithm is effective in solving the multi-objective optimization model.

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“…Previously, researchers have done different literature reviews about the deployment of AI in AECO (Loyola, 2018;Aibinu, Koch and Ng, 2019;Duan, Edwards and Dwivedi, 2019;Darko et al, 2020;Hong et al, 2020;Abioye et al, 2021;Pan and Zhang, 2021;Debrah, Chan and Darko, 2022). However, none focuses on AM areas, which comprise a broad list of disciplines.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

Rampini¹,

Cecconi²

2022

ITcon

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The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning.

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Artificial intelligence in green building

Cited by 115 publications

References 120 publications

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Technology Innovation for Sustainability in the Building Construction Industry: An Analysis of Patents from the Yangtze River Delta, China

Multi-objective integrated optimization study of Prefabricated building projects introducing sustainable levels

Artificial intelligence in construction asset management: a review of present status, challenges and future opportunities

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