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.
Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.