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Buildings remain pivotal in global energy consumption, necessitating a focused approach toward enhancing their energy efficiency to alleviate environmental impacts. Precise energy prediction stands as a linchpin in optimizing efficiency, offering indispensable foresight into future energy demands critical for sustainable environments. However, accurately forecasting energy consumption for individual households and commercial buildings presents multifaceted challenges due to their diverse consumption patterns. Leveraging the emerging landscape of the Internet of Things (IoT) in smart homes, coupled with AI-driven energy solutions, presents promising avenues for overcoming these challenges. This study introduces a pioneering approach that harnesses a hybrid deep learning model for energy consumption prediction, strategically amalgamating convolutional neural networks' features with long short-term memory (LSTM) units. The model harnesses the granularity of IoT-enabled smart meter data, enabling precise energy consumption forecasts in both residential and commercial spaces. In a comparative analysis against established deep learning models, the proposed hybrid model consistently demonstrates superior performance, notably exceling in accurately predicting weekly average energy usage. The study's innovation lies in its novel model architecture, showcasing an unprecedented capability to forecast energy consumption patterns. This capability holds significant promise in guiding tailored energy management strategies, thereby fostering optimized energy consumption practices in buildings. The demonstrated superiority of the hybrid model underscores its potential to serve as a cornerstone in driving sustainable energy utilization, offering invaluable guidance for a more energy-efficient future. [ABSTRACT FROM AUTHOR]

Rammed earth in a built environment has vapor diffusion characteristics and humidity control abilities, each of which is respectively attributed to the porous structures and the hydrophilic properties. Indeed, these structures and properties allow for the easy absorbance of water particles, hence affecting the durability of a rammed earth wall. This paper presents the water-repellency method for rammed earth walls, which utilizes siloxane copolymers containing fluorine. The water-repellent properties are investigated by measuring the contact angle, water absorption rate, and compressive strength after spray-coating with the synthesized siloxane copolymers on the surface of the rammed earth specimens under study. The water contact angle of the specimen, coated with a siloxane copolymer containing 10 mol.% of a silane monomer with a fluorine group, is about 140°. The water absorption of the specimen obtained after immersing in water for 24 h is low, at about 3.5 wt.%. In addition, the compressive strength remains more than 80% of the corresponding strength of the specimen which is not immersed in water. It is confirmed that the use of a 10% by volume of the siloxane copolymer containing the fluorine group may enhance the water-repellent performance and economic competitiveness. [ABSTRACT FROM AUTHOR]

Prestressed concrete (PC) slab using tendons is one of the most frequently used slab systems in the construction of buildings with long-span slabs. To simultaneously minimize the construction cost and the environmental impact, a green design model for PC slabs in long-span structures is necessary. In this paper, a multi-objective green design model for prestressed concrete slabs (MGDPCS) was developed to minimize both CO2 emissions and the construction costs of PC slabs. MGDPCS provides the optimized PC slab thickness, diameter and yield strength of the rebar, size and yield strength of the tendon using the Non-dominated Sorting Genetic Algorithm (NSGA-II) for the input PC slab size and load. Furthermore, the effects of changes in the long- and short-side of span and tendons of PC slabs on construction costs and environmental impact are analyzed using the proposed model. Accordingly, we developed two indicators, that is, the environmental and economic scores and the eco-friendly coefficient, to evaluate the performance of the practical green designs using MGDPCS. To verify the applicability of MGDPCS, the model was applied used to analyze the designs of PC slabs in an actual six-story industrial building with a slab span of 10 m × 10 m. The results showed that the optimal designs obtained from MGDPCS outperformed existing slab designs for buildings by 8.12% and 13.62% based on the reductions in CO2 emissions and costs, respectively. [ABSTRACT FROM AUTHOR]