The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector.

Earth has been used as a material for the building in the form of red bricks, sand mortar, mud floors, and lime sand ceiling since the earlier civilization of mankind. Due to its numerous climatic changes and the lack of standards, the earth’s use is decreased. Due to potential capacity and less energy consumption in producing compressed earth blocks exhibits more promising structures than other earth materials. This review article illustrates that using compressed earth blocks with natural fibers will be an excellent alternative for conventional materials. Also, the paper discusses the thermal comfort exerted by naturally available materials such as banana fiber, coconut fiber, sisal fiber, palm fiber, and cassava peels. Natural fibers also increase the mechanical behavior such as compressive, tensile, and water absorption capability of these blocks, which is environmentally friendly and exhibits better thermal stability. This study aims to design an earth-building material with a minimum environmental impact and more stability.

This study investigates the recycled wastes and optimizes the raw materials in the performance of cement. The research emphasis on characteristics of oceanic waste calcium and steel manufacturing waste as a cement replacement, and its target is to achieve a green environment to reduce global warming. The hybrid cement periphrastically reduces the raw material usage in the manufacturing of cement. It will improve the calcium silicate hydrates formation in the ideal transition phase of the binder. In this study, Taguchi methods are followed in mix design proportion of cement mortar including water-cement ratio (0.45,0.5 and 0.6) and results in the compressive strength in three different ages (7th, 14th and 28th days) were observed. The bulk crystalline constituent patterns are carried out in different magnifications using Scanning Electron Microscope, and its pore structure is studied. The results indicate the composition of pores due to the fineness of hybrid material causes poor flow value. Hence, the use of superplasticizer (SP) to attain the consistency of the mortar to improve the workability and the mechanical properties of the hybrid material composites.

This paper demonstrates the validation and prediction of the wall temperature of a building exposed to a composite climate. Two artificial intelligence models, such as multiple linear regression and artificial neural networks, have been used to predict. The wall temperature has been predicted mainly based on the parameters like ambient temperature, wind speed and relative humidity in all four directions of the buildings. Three statistical analyses were used to validate the model's outcome: R-Squared, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). In terms of train and control data, the two models yield comparable findings. The artificial neural network model has more adaptability since it was able to adjust to unexpected changes in the input data, according to a comparison of the applied mathematics of each model. The regression model was used for this investigation because it gives constant estimate values for the factors, though the neural model isn't numerically characterized. The review infers that the neural model must be utilized as an additional way to predict the wall temperature. This study can plan and adapt buildings for future work, considering the most critical climatic conditions. It also assists architects and engineers in determining the appropriate insulation for the building envelope, which improves its thermal performance.