This study presents a multi-target particle swarm optimization (MT-PSO) approach for efficient concrete mix design. It simultaneously designs mixes with multiple predefined strengths under a constant water-cement ratio. A gradient boosting-based surrogate model, trained on experimental mix data, predicts compressive strength. The modified particle swarm optimization (PSO) algorithm accommodates multiple targets in parallel, allowing solution sharing across target groups. MT-PSO is compared with a repeated PSO (R-PSO) strategy that optimizes each target separately, both minimizing the absolute error between predicted and desired strengths. Across 30 independent trials, MT-PSO consistently achieves lower mean errors, smaller deviations, and faster convergence, often reaching R-PSO's final accuracy within only a few iterations. Moreover, MT-PSO requires over 85% fewer fitness evaluations. These results demonstrate the superior accuracy, robustness, and computational efficiency of MT-PSO for multi-target optimization problems.

Asphalt pavement consists of aggregates resulting in a waste material at end of its life. The aggregates can be reused as basic material for asphalt or cementitious binding agents. In both scenarios, the recycled aggregates should provide a good bond with the binder to achieve strength. This study focuses on reusing recycled asphalt aggregates (RAA) in mortar. The major weakness of RAA is the thin oily film originating from the asphalt residue, weakening the bond with cement. The pyrolysis method is accessed in an attempt to overcome this weakness. Three scenarios were investigated; the use of virgin aggregates (VA), RAA, and pyrolysis recycled asphalt aggregate (PRAA) as constituent in mortar. All variables were set a constant except for the aggregate type, the VA mortar function as controlling element. This research is methodologically based on experimental data conducted in the laboratory, while aggregate samples were taken from the field. To analyze the influence of pyrolysis to the aggregate-to-cement bond behaviour, qualitative and quantitative data were collected. The quantitative data were the mechanical properties, the mortar tensile, and compression strength. The qualitative data were obtained from scanning electron microscope readings to visually observe the aggregate surface roughness and voids, including the aggregates cross-section and pre-existing micro-cracks in the aggregate-to-cement interface. Supporting data were the aggregates‘ abrasion rate and absorption. The RAA resulted in a significant mortar strength decrease. This conclusion was supported by the findings of pre-existing cracks in the interfacial transition zone. The pyrolysis method improved the compression strength but negligibly affected the tensile behavior. The compression and tensile strength increased as a function of time for both RAA and PRAA, and a strength convergence was reached at 28 days. The PRAA is considered an option for reuse in mortar, supporting nature conservation.

Natural gas vehicles (NGV) face significant adoption barriers in Jakarta. Therefore, a successful transition requires measures from the government. Owing to the high cost of transition policies, the efficacy of these policies must be analyzed to identify the most effective policy. The implementation of transition policies, however, could dynamically influence people's perception and behavior, which then changes the landscape of adoption barriers. Furthermore, even a seemingly successful policy may fail when a certain pathway of uncertainties emerges in the future. To address these concerns, we integrated agent-based modeling, exploratory modeling, and diffusion of innovation theory into the exploratory model-based diffusion analysis approach. This approach evaluates the policy's performance, explores changes in the relative importance of different adoption barriers, and identifies policy vulnerabilities, i.e., scenarios leading to policy failures. We tested this approach on four NGV transition policies targeting three adoption barriers. We found that the importance of adoption barriers and the critical uncertainties upon the implemented policies. The social–behavioral barrier predominates under current conditions, whereas the economic factor becomes more relevant when all policies are executed. Understanding the changes in adoption barriers and policy vulnerabilities will help decision-makers to prepare additional measures that ensure a successful transition.