Fatigue Analysis of Bitumen Modified with Composite of Nano-SiO2 and Styrene Butadiene Styrene Polymer

Since fatigue cracking is caused in the middle-temperature conditions due to the stresses from heavy traffic and as the bitumen plays a very important role in controlling this failure, therefore, in recent years, the production of the modified bitumen that can give a good performance in the middle temperatures has always attracted the interest of researchers. One of these bitumen modifiers is the styrene butadiene styrene (SBS) polymer. Due to the phase separation of bitumen and polymer, aging and oxidation, this polymer may not exhibit expected field performance at middle temperatures. Therefore, in this research, it is attempted to analyze the middle-temperature performance using the combination of nano-SiO2 and SBS polymer in the bitumen modification. In this paper, the addition of SBS and nano-SiO2 to the base bitumen resulted in the reduction of the complex modulus, phase angle, storage modulus and loss modulus at middle temperatures, thereby improving the potential of fatigue failure resistance. In general, considering the requirement for the rotational viscosity value up to 3 Pa.s at 135 °C and also, regarding the economic issues in choosing a lower percentage, the combination of 4.5% SBS + 3% nano-SiO2 is selected as the optimal composite

New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load (LC) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the LC pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings

New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load (LC) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the LC pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings