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.

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 (L_C) 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 L_C 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.

Recently, symmetric networks have received much attention in various applications. They are a single route for incoming and outgoing network traffic. In symmetric networks, one of the fundamental categories of wide-ranging scheduling problems with several practical applications is the FSSP. Strictly speaking, a scheduling issue is found when assigning resources to the activities to maximize goals. The difficulty of finding solutions in polynomial time makes the flow shop scheduling problem (FSSP) NP-hard. Hence, the utilization of a hybrid optimization technique, a new approach to the flow shop scheduling issue, on symmetric networks is given in the current research. In order to address this issue, each party’s strengths are maximized and their weaknesses reduced, and this study integrates the Ant Colony Algorithm with Particle Swarm Optimization (ACO-PSO). Even though these methods have been employed before, their hybrid approach improves their resilience in a variety of sectors. The ACO-PSO is put to the test by contrasting it with innovative algorithms in the literature. The search space is first filled with a variety of solutions by the algorithm. Using pheromones in the mutual region, the ACO algorithm locally controls mobility. Moreover, the PSO-based random interaction among the solutions yields the global maximum. The PSO’s random interaction among the solutions typically results in the global maximum. The computational research demonstrates that the recommended ACO-PSO method outperforms the existing ones by a large margin. The Friedman test also shows that the average algorithm ranks for ACO and PSO are 1.79 and 2.08, respectively. The proposed method has an average rank of 2.13 as well. It indicates that the suggested algorithm’s effectiveness increased.