The construction industry faces persistent challenges, including labor shortages and safety hazards, while traditional construction methods are increasingly strained by the complexity and sustainability demands of modern projects. The integration of robotics shows significant potential for mitigating labor shortages and enhancing safety on construction sites. The current adoption of robotics technologies is driven by both the maturity of robotics technology and the potential for cost reduction compared with manual labor. This review synthesizes recent advancements and trends in construction robotics through a bibliometric analysis of 212 publications indexed in Web of Science from 2002 to 2024. Key findings indicate a 320% increase in research output from 2015 to 2022, with dominant clusters focusing on autonomous navigation, human–robot collaboration, and sustainability-driven automation. Geographically, China and the United States lead in number of publications, with 67 and 65 articles, respectively; however, cross-border collaborations remain sparse, constituting fewer than 5% of co-authored papers. Keyword co-occurrence analysis reveals evolving priorities, including artificial intelligence (AI)-driven adaptive control, modular prefabrication, and the ethical implications of automation. Despite technological advancements, critical gaps remain in terms of interoperability, workforce retraining, and regulatory frameworks. This study emphasizes the need for interdisciplinary integration, standardized protocols, and policy alignment to bridge the divide between academic innovation and industry adoption, ultimately facilitating the global transition toward Construction 4.0.

The visual aesthetics of villages are remarkably affected by the exterior quality of traditional residences, influencing the impression and assessment of local culture. A proper scientific assessment of exterior quality can protect traditional cultures and improve the development of villages. This research was conducted in a village consisting of 115 residences (Mengjinglai village, which is on the border between China and Myanmar). The backpropagation (BP) neural network model with genetic algorithm (GA) was applied to evaluate the quality of the dwellings. All the evaluation values of the dwellings were defined by scores. Meanwhile, the score of each residence was affected by three main factors: architectural spatial elements, architectural construction elements, and historical and cultural elements. The results show that the village’s dwellings are well preserved and clearly express the traditional Dai style. Moreover, the GA–BP approach is more suitable than the traditional BP method for the assessment of the exterior quality. The quantitative machine learning model would be useful for other aspects of the assessment of similar villages in the future.

This chapter explores how spatial narrative theory transforms traditional village conservation by treating villages as dynamic narrative systems integrating architecture, memory, and heritage. The proposed “decode-reconstruct-activate” methodology deciphers cultural meanings embedded in spatial structures while digital tools like AR enhance engagement. Emphasizing audience-centric design, it examines tailored approaches for tourists, scholars and communities, balancing technological innovation with cultural authenticity. Case studies demonstrate practical applications, while theoretical discussions redefine conservation paradigms through community empowerment and living authenticity. The analysis concludes with future directions in digital ethics and resilient narratives, offering a model for sustainable heritage communication that prioritizes cultural continuity over static preservation