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This study aims to optimize the design of reinforced concrete retaining walls to achieve the lowest possible cost while maintaining structural stability and compliance with standards.

The Rao1 algorithm was employed to optimize the design, considering seven independent variables related to geometry and four variables related to the reinforcement ratio. Geotechnical and structural analyses were performed using the Coulomb theory for static loads and the Mononobe–Okabe theory for dynamic loads. Cost calculations included concrete volume and reinforcement quantities across varying horizontal and vertical acceleration coefficients in four scenarios.

The optimization process revealed that the Rao1 algorithm consistently identified design variables near the lower regulatory limits, achieving the same cost across all scenarios. The results demonstrate that the Rao1 algorithm is an effective tool for optimizing the design of self-supporting reinforced concrete retaining walls, ensuring both cost efficiency and compliance with structural requirements.

In this study, the aim was to achieve an optimal, cost-effective design for retaining walls composed of a two-stage stem structure. Unlike conventional designs that utilize single-tier retaining walls, this study emphasizes the use of a two-stage stem configuration. It was noted that the Rao1 algorithm has not been previously applied to two-stage stem retaining walls in the literature. By applying the Rao1 algorithm, it was observed that a design meeting both minimum cost and required criteria was successfully achieved. Therefore, it was concluded that the Rao1 algorithm could be a valuable tool for retaining wall design. This study differs from other works in the literature both in terms of the two-stage stem-retaining wall design and the algorithm used.

The purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently.

In this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities.

The results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies.

This research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.

This study attempts to control the movement of industrial robots with virtual and real-time variable time delay. The improved variable wave method was used for analyzing position tracking performance and stability of the system.

This study consists of both theoretical and real-time operations. Teleoperation systems that provide information about point or environment that people cannot reach and are one of the important robotic works that include the human–machine interaction technology were used to obtain the necessary data. Robots, as the simulated virtual environment to achieve real behaviors, were found to be important for the identification of damage that may occur during the tests performed by real robots and then in terms of prevention of errors identified in algorithm development stages.

The position and speed controls of the real–virtual–real robots consist of the teleoperation system. Also, in this study, the virtual environment was created; variable time delay motion control with teleoperation was performed and applied in the simulation and real-time environment; and the performance results were analyzed.

The teleoperation system created in the laboratory consists of a six-degree-of-freedom (dof) master robot, six-dof industrial robot and six-dof virtual robot. A visual interface is designed to provide visual feedback of the virtual robot’s movements to the user.