External shading geometry on buildings has been found to contribute substantially to reducing energy consumption for cooling. This study examines the effect of inclined wall self-shading strategy on heat gain in an office building. Field measurement of environmental variables such as ambient temperature, relative humidity, dew point, and wet bulb temperature was carried out in a case study inclined wall self-shading office building located in Putrajaya, Malaysia. The results of the validation of ApacheSim simulation software tool against the measured environmental variables indicated significant reliability having Pearson correlations ranging from 0.56 to 0.90. In establishing the relationship between different inclined wall strategies to the amount of heat gain, modification of the inclined wall self-shading projection (SSP) was modelled and experimented using ApacheSim simulation. Findings from the analysis revealed a relationship between heat gains into a building space and self-shading projection (SSP), as heat gains tend to reduce with increased SSP. From the findings, the optimum inclination angle of self-shading for effective heat gain reduction is based on a 45% self-shading projection. The application of inclined wall self-shading strategy in buildings would, therefore, bring about a reduction in heat gain, which invariably reduces energy consumption for cooling.
Using the soil and water assessment tool (SWAT), runoff in pervious and impervious urban areas was simulated in this study. In the meantime, as a novel application of machine learning, the emotional artificial neural network (EANN) model was employed to enhance the SWAT obtained for this study. As a result of the EANN model's capabilities in rainfall-runoff phenomena, the SWAT-EANN couple model has been used to assess urban flooding. The pervious, impervious, and water body areas of the study area were classified and mapped to estimate the cover change over three epochs. Land use map, precipitation data, temperature (minimum and maximum) data, wind speed, relative humidity, soil map, solar radiation, and digital elevation model were used as inputs for modelling rainfall-runoff of the study area in the ArcGIS environment. The accuracy assessment of this study was excellent (root-mean-square error 1 mm of precipitation). It also revealed that (a) a land use map illustrating changes in impervious, pervious surface, and water body for 1998, 2008, and 2018; (b) runoff modelling using a historical pattern of rainfall-runoff changes (1998-2018); and (c) descriptive statistical analysis of the runoff results of the research. This research will aid in urban planning, administration, and development. Specifically, it will prevent flooding and environmental problems.
This paper presents the global research landscape and scientific progress on occupant thermal comfort in naturally ventilated buildings (OTC-NVB). Despite the growing interest in the area, comprehensive papers on the current status and future developments on the topic are currently lacking. Hence, the publication trends, bibliometric analysis, and systematic literature review of the published documents on OTC-NVB were examined. The search query "Thermal Comfort" AND "Natural Ventilation" AND "Buildings" was designed and executed to recover related documents on the topic from the Elsevier Scopus database. Results showed that 976 documents (comprising articles, conference papers, reviews, etc.) were published on the topic from 1995 to 2021. Further analysis showed that 97.34% of the publications were published in the English language. Richard J.de Dear (University of Sydney, Australia) is the most prolific researcher on OTC-NVB research, while Energy and Buildings has the highest publications. Bibliometric analysis showed high publications, citations, keywords, and co-authorships among researchers, whereas the most occurrent keywords are ventilation, natural ventilation, thermal comfort, buildings, and air conditioning. Systematic literature review demonstrated that OTC-NVB research has progressed significantly from empirical to computer-based studies involving complex mathematical equations, programs, or software like artificial neural networks (ANN) and computational fluid dynamics (CFD). In general, OTC-NVB research findings indicate that physiological, social, and environmental factors considerably influence OTC in NVBs. Future studies will likely employ artificial intelligence or building performance simulation (BPS) tools to examine relationships between OTC and indoor air/environmental quality, human behavior, novel clothing, or building materials in NVBs.
This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.
Reinforced concrete (RC) constructions are seriously threatened by chloride-induced corrosion (CIC) and carbonation, which can result in structural degradation, safety issues, and financial losses. Electrochemical methods and microstructural analysis tests are some of the laboratory techniques used to examine key elements of CIC, such as the impact of different variables and the efficacy of mitigation solutions. In situ studies that make use of non-destructive testing, chloride profiling, and half-cell potential measurements offer important new insights into the long-term performance and causes of RC structure deterioration in real-world circumstances. Non-destructive approaches for CIC detection are emerging these days and provide fruitful results. Studies have focused on the use of these approaches for CIC detection on small specimens in the lab as well as on full-scale experiments in the field. This review covers both in situ monitoring and laboratory studies to provide a thorough analysis of CIC.