To maximize the benefits without sacrificing the functionality of projects, sustainability concepts should be used across all stages of the decision-making process when creating residential buildings. The primary sustainable aims may be improved with BIM activities. However, in the building sector of underdeveloped nations, BIM activities use informal methods. By examining the connection between overcoming BIM implementation challenges and the overall sustainable success (OSS) in building projects, this research seeks to establish a model for BIM implementation. Following the BIM hurdles identified in earlier research, 86 building stakeholders in the Egyptian building sector were given questionnaires. The structure of the obstacles was established and confirmed using partial least-squares structural equation modeling (PLS-SEM), and the connections between the OSS and overcoming BIM deployment were investigated. The adoption of BIM contributed 40.7% to the project’s long-term sustainability, according to the data, which demonstrated a strong link. The findings of this research will serve as a roadmap for decision-makers who want to use BIM in developing nations’ building sectors to save costs and increase sustainability.

Omni-wheeled mobile robots (Omni WMRs) are commonly used in indoor navigation applications like surveillance, search and rescue, and autonomous transportation. They are always characterized by their versatility, mobility and high payload. This paper presents the mechatronic design, low-level control and high-level control of an indoor 4 Omni-Wheeled Mobile Robot (4OWMR). Since autonomy and path planning are research necessities for WMRs, four heuristic and probabilistic path-planning techniques are chosen for experimental implementation. The selected techniques are PRM (Probabilistic Roadmaps), RRT (Rapidly exploring Random Tree), RRTSTAR (RRT*), and ASTAR (A*) algorithms. The proposed environments are static, expressed by maps with unknown nodes and obstacles. Local path planning is implemented with simultaneous localization and mapping (SLAM). Path planning techniques are programmed, and the obtained paths are optimized by a multi-objective genetic algorithm technique to ensure the shortest path and its smoothness. The optimized paths are deployed to the 4OWMR. The obtained results are compared in terms of travel time, travel distance, average velocity and convergence error. A ranking technique is utilized to rank the obtained results and show the most preferred technique in terms of energy consumption and convergence accuracy in addition to the overall ranking. Experimental results showed that the Hybrid A* algorithm produced the best-generated paths with respect to other techniques.

Although sustainability has been an issue in the built environment for some time, it has not yet been fully addressed in sustainable interior architecture and design. This research aimed to identify and analyze obstacles preventing sustainable interior architecture and design implementation. As a result, the partial least structural equation modeling approach (PLS-SEM) has been requested to evaluate these obstructions. Therefore, after identifying 30 possible barriers through a literature search, a survey questionnaire was issued to 100 interior designers to evaluate their significance. According to the findings, governmental obstacles are the most pressing, followed by those related to information, knowledge, awareness, technology, training, attitudes, the market, and economics. This study’s conclusions may benefit professional interior architects, designers, academics, statutory authorities, administrations, and politicians. The current study addresses the lack of prior literature by compiling a thorough inventory of obstacles to environmentally friendly interior architecture and design, lays the framework for more in-depth future studies, and uses a unique PLS-SEM that has not been used previously.

The control of pneumatic soft robotics is challenging due to nonlinearites arising from many factors including pneumatic system components and material properties of the soft actuator. Manual methods for PID controller tuning are inadequate for the nonlinear and time-variant dynamics present in soft robotics. Affordable pneumatic components such as on/off valves cause discontinuities in flow rate, introducing nonlinearities and oscillatory fluctuations into the system. This study proposes a dual-loop control system: one for PID and Fractional-Order PID (FOPID) control of a solenoid valve that feeds air into the actuator, and another for PID control of the pump upstream of the valve. The PID and FOPD parameters are optimized using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Simulations and real-world experiments are conducted to validate the optimized parameters. Our results demonstrate that the dual-loop hardware configuration reduces fluctuations from the valves compared with a single-loop control scheme. The experimental statistical analysis confirms that FOPID achieves the highest significant improvements in rise time (PSO) and peak time (GA, PSO), while PID performs better for overshoot (GA, PSO). These findings highlight the importance of selecting an appropriate optimization algorithm based on the specific control objective, as FOPID does not outperform PID in every metric across all methods.

The control of soft elastomeric actuators via pneumatic systems presents challenges due to system nonlinearities and oscillatory behavior. The control of soft robotics is an underexplored field compared to the control of traditional robotics. This study explores evolutionary algorithms for auto-tuning Proportional-Integral-Derivative (PID) controllers in pneumatic soft robotics. Four optimization algorithms, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Surrogate-Based Optimization (SBO), were employed to optimize PID parameters for pneumatic pressure control. Each algorithm was tested with four objective functions: Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), and Integral Time Square Error (ITSE). Both simulated and experimental studies are conducted to evaluate these algorithms using a pneumatic system designed with affordable on/off valves controlled by Pulse Width Modulation (PWM). Key performance metrics were analyzed: rise time, settling time, overshoot, peak value, and peak time. Results indicate that PSO and GA offer faster response times and moderate overshoot, while SA provides minimal overshoot at the cost of slower response times. These findings can significantly contribute to the practical control of pneumatic systems in soft robotics, offering insights for future optimization and application development.

Four Omni Wheels Mobile Robot (4OWMR) are widely utilized for indoor navigation applications such as autonomous transportation, surveillance, rescue, and search. This paper proposes a ranking approach to select the dynamic path planning algorithm. The selection process involves implementing the four path planning techniques (PRM, RRT, RRT*, Hybrid A*) on three different maps. The Weighted Sum Model (WSM) is then applied to each map to rank these algorithms based on several factors. Finally, the points of each algorithm in the three maps are added up, and the algorithm with the highest score is selected as the dynamic path planning algorithm. The results indicate that the Hybrid A* algorithm surpasses the others, scoring 46 points, which represents a 21.7% increase compared to PRM and RRT, and a 30.4% increase compared to RRT* when using the weighted sum ranking technique. Therefore, it can be selected as the dynamic path planning algorithm.