Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.

The construction business is always changing, and with the introduction of artificial intelligence (AI) technology it is undergoing substantial modifications in a variety of areas. The purpose of this research paper is to investigate the function of AI tools in the construction industry using a hybrid multi-criteria decision-making (MCDM) framework based on the Delphi method, analytic network process (ANP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under a fuzzy scenario. The ANP framework offers a systematic approach to quantifying the relative importance of AI technologies based on expert opinions gathered during the Delphi process, whereas the fuzzy TOPSIS methodology is used to rank and select the most appropriate AI technologies for the construction industry. The final results from the ANP revealed that the technological factors are the most crucial, followed by the environmental factors, which highly influence the AI environment. In addition, TOPSIS identified robotics and automation as the best AI alternative among the three options, followed by building information modeling (BIM), whereas computer vision was the least preferred among the list. The proposed hybrid MCDM framework enables a comprehensive evaluation and selection process that takes into account the interdependencies between AI technologies and uncertainties in decision-making.

With the improvement of technologies, people’s demand for intelligent devices of indoor and outdoor living environments keeps increasing. However, the traditional control system only adjusts living parameters mechanically, which cannot better meet the requirements of human comfort intelligently. This article proposes a building intelligent thermal comfort control system based on the Internet of Things and intelligent artificial intelligence. Through the literature review, various algorithms and prediction methods are analyzed and compared. The system can automatically complete a series of operations through IoT hardware devices which are located at multiple locations in the building with key modules. The code is developed and debugged by Python to establish a model for energy consumption prediction with environmental factors such as temperature, humidity, radiant temperature, and air velocity on thermal comfort indicators. By using the simulation experiments, 1700 data sets are used for training. Then, the output PMV predicted values are compared with the real figure. The results show that the performance of this system is superior to traditional control on energy-saving and comfort.

This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors amidst the growing complexity of projects and the increasing integration of AI technologies. The methodology employed involves the utilization of three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors, such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the influential factors identified via MICMAC provide insights for prioritizing strategic interventions. The implications of this study extend to informing decision-makers in the civil engineering domain about effective strategies for leveraging AI in their cost management practices. By adopting a systematic MCDM approach, stakeholders can enhance project outcomes while optimizing resource allocation and mitigating financial risks.

The geographical distribution characteristics of villages characterised by ethnic minorities are determined by the selection of the site when the village was initially established. The location of inherited and well-preserved minority villages must be exceptionally compatible with the natural terrain, with a logical relationship. Nonetheless, the issue of village location, which is directly related to the development of the features of the geographical distribution, has received little attention from scholars. The average nearest proximity index, Voronoi, kernel density analysis, proximity analysis, and the Geographical Detector (GeoDetector) were used to analyse the geographic distribution characteristics of villages and their correlation with terrain, as well as the difference between the influence of each terrain factor. The findings indicated the following. (1) The geographical distribution of minority villages in Fujian Province is of the agglomeration type, with a significant “mononuclear” feature, and the topography has a facilitating effect on the clustering distribution of villages. (2) The geographical distribution of minority villages in each city of Fujian Province coexisted with the agglomeration type and the dispersion type, and the role of topography in promoting the agglomeration-type distribution of villages was not affected by the distribution density of villages. (3) The site selection of Fujian-minority villages is characterised by medium altitude, moderate slope, sun exposure, and no obvious hydrophilicity. Minority villages are mainly located in areas with an elevation of 202–647 m; a slope of 6–15°; a flat land aspect with a south slope, southeast slope, or southwest slope; and distance of 500–1500 m from 5–20 m wide rivers of level 2. (4) The site selection of Fujian minority villages is influenced by various topographic factors, such as elevation, slope, aspect, river buffer, river width, and river level, among which river width has the most substantial effect. (5) All topographic factors have a two-factor enhancing relationship with each other, aspect and slope have the most substantial effect and play a dominant role in site selection. The research findings illuminate the internal logic of the geographical distribution differentiation of villages characterised by ethnic minorities, which is critical for promoting the protection of modern ethnic-minority villages.

Building energy waste has become one of the major challenges confronting the world today, so specifications and targets for building energy efficiency have been put forward in countries around the world in recent years. The schematic design stage matters a lot for building energy efficiency, while most architects nowadays are less likely to make energy efficiency design decisions in this stage due to the lack of necessary means and methods for analysis. An integrated multi-objective multivariate framework for optimization analysis is proposed for the schematic design stage in the paper. Here, the design parameters of the building morphology and the design parameters of the building envelope are integrated for analysis, and an integrated performance prediction model is established for low-rise and medium-rise residential buildings. Then, a comparison of the performance indicators of low-rise and medium-rise residential buildings under five typical urban climatic conditions is carried out, and the change patterns of the lighting environment, thermal environment, building energy demand, and life cycle cost of residential buildings in each city under different morphological parameters and design parameters of the building envelope are summarized. Specific analysis methods and practical tools are provided in the study for architectural design to ensure thermal comfort, lighting comfort, low energy consumption, and low life-cycle cost requirement, and this design method can inspire and guide the climate adaptation analysis and design process of low-rise and medium-rise residential buildings in China, improve architects’ perception of energy-saving design principles of low-rise and medium-rise residential buildings on the ontological level, as well as provide them with a method to follow and a case to follow in the actual design process.