Objectives The development of big data and the internet of things (IoT) has accelerated the process of artificial intelligence, with machine learning (ML) becoming particularly noticeable. More people are realizing the influence of advanced technology on reframing the researches in the field of sustainable built environment. Thus, this research aims to identify the role of machine learning in the research on sustainable built environment.
Methods By reviewing the conceptions of sustainability and built environment, the research summarizes several important topics in the wide-ranging field of sustainable built environment. Based on literature from Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) over the last five years, the research finds that the increasing trends of ML-related research are significant for topics such as urban public health, energy consumption and carbon emission, climate and environment, ecosystem, and green travel, which may serve as a useful clue for structure organization and conclusion drawing of this research. Initially, the research elaborates on the concept, classification and algorithms of ML, and presents the application characteristics of strong prediction capability but poor interpretation capability. Then the research analyzes the trend change of ML from predictability to interpretability, followed by the descriptions of scope, taxonomy and core algorithms of interpretable ML. Moreover, the research establishes a thorough table to list multiple ML algorithms and their roles in the research. Far from being all-inclusive, the research only tries to incorporate as many methods and objectives as possible across the world. The research particularly focuses on the predictability and interpretability of the results of ML algorithms, and the specific roles of such algorithms in the research direction, methodology and conclusion of sustainable built environment.
Results The results show that ML’s interpretability capability substantially aids in the transformation of research techniques. More and more researchers are practicing ML-based research frameworks and building practical methodological routes, accompanied by increasingly diversified research directions and objects. ML has excellent performance in dealing with complex, heterogeneous, dynamic and large-scale data. Complicated issues previously considered difficult to solve have become the deciphering objects of ML, providing new insights and possibilities for solving composite puzzles. Meanwhile, ML itself is progressing toward intelligence, transparency and interpretability. By integrating experience and human wisdom, ML can help researchers formulate optimal decision strategies, reveal underlying mechanisms, and uncover universal laws. Furthermore, the research findings are more scientific and sophisticated. The majority of researches in the five key topics recognize the great advantages of ML in prediction and interpretation and start to develop interpretable and readable methods based on specific scenarios and problems. It is clear that more researches are focusing on understanding decision-making processes and mining patterns, although the levels of progress thereof vary depending on specific issues. As for public health, several articles discuss the effects of the built environment on health concerns. The applications of interpretable ML are relatively simple and preliminary, with less empirical investigation of transparent models. More medical and clinical experiments are needed to support theoretical analyses. As for energy consumption and carbon emission, as well as climate and environment, both predicting and interpreting tasks are important. By upgrading data processing, the accuracy of estimation and prediction can be improved. Additionally, models’ inner processes can be better understood by optimizing algorithms. As for ecosystems, there are more researches on prediction than on interpretation. The complexity of ecological elements might be the primary cause for this situation, which may be changed when more powerful models are created. As for green travel, ML applications have reached a mature stage. Lots of cutting-edge technologies have been developed and widely implemented. Except for the predictable and interpretable tasks, some researches have further stepped into the next level of intelligent decisions and optimal strategies, providing a vibrant foundation for human-machine interaction.
Conclusions In summary, there is no doubt that ML plays an important role in transforming research methods, objects and conclusions in the field of sustainable built environment, and can effectively promote the development of researches in this field in a more quantitative, diverse, intelligent and scientific manner. It can be foreseen that the researches and applications of ML in sustainable built environment will continue to advance swiftly, with long-term and broad prospects. Besides, the shift from predictability to interpretability is obvious. On the one hand, there are more interpretable methods and readable ML models, and research objectives tend to emphasize understanding the decision-making process and revealing the underlying rules; on the other hand, the exploration of casual mechanisms based on clinical experiments is still insufficient. In terms of the five key topics, the development of ML research and application is the fastest in transportation issues such as green travel, forming a mature system of prediction, interpretation, and decision-making, while continuously promoting the exploration of more innovative applications and methods of human-machine interaction. There are also quite full prediction and interpretation tools for researches on energy consumption and carbon emission, and climate and environment. However, both the variety and professionalism of research methods remain insufficient. Though the ML applications in ecosystems and public health are still in the early stage, many researchers have realized the great potential of ML in these areas, implying that additional chances and breakthroughs in related fields are likely to emerge in the future.
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