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There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour based on the available data are required. Consequently, this research proposes a hybrid intelligent system, Fuzzy Group Method of Data Handling (GMDH)–Horse herd Optimisation Algorithm (HOA), for predicting one of the most important parameters in concrete structure design, compressive strength. In order to avoid uncertainty in the modelling process, crisp input values were converted to Fuzzy values (Fuzzification). Next, using Fuzzy input variables, the group method of data handling is used to predict the compressive strength of recycled aggregate concrete. The HOA algorithm is one of the newest metaheuristic algorithms being used to optimise the Fuzzy GMDH structure. Several databases containing experimental mix design records containing mixture components are gathered from published documents for compressive strength to assess the accuracy and reliability of the proposed hybrid Fuzzy-based model. Compared to other original approaches, the proposed Fuzzy GMDH model with the HOA optimiser outperformed them in terms of accuracy. A Monte Carlo simulation is also employed for uncertainty analysis of the empirical, standalone, and hybridised models in order to demonstrate that the evolutionary Fuzzy-based approach has less uncertainty than the standalone methods when simulating compressive strength.

Purpose Reliability-based design optimizations (RBDOs) of engineering structures involve complex non-linear/non-differentiable performance functions, including both continuous and discrete variables. The gradient-based RBDO algorithms are less than satisfactory for these cases. The simulation-based approaches could also be computationally inefficient, especially when the double-loop strategy is used. This paper aims to present a pseudo-double loop flexible RBDO, which is efficient for solving problems, including both discrete/continuous variables. Design/methodology/approach The method is based on the hybrid improved binary bat algorithm (BBA) and weighed simulation method (WSM). According to this method, each BBA’s movement generates proper candidate solutions, and subsequently, WSM evaluates the reliability levels for design candidates to conduct swarm in a low-cost safe-region. Findings The accuracy of the proposed enhanced BBA and also the hybrid WSM-BBA are examined for ten benchmark deterministic optimizations and also four RBDO problems of truss structures, respectively. The solved examples reveal computational efficiency and superiority of the method to conventional RBDO approaches for solving complex problems including discrete variables. Originality/value Unlike other RBDO approaches, the proposed method is such organized that only one simulation run suffices during the optimization process. The flexibility future of the proposed RBDO framework enables a designer to present multi-level design solutions for different arrangements of the problem by using the results of the only one simulation for WSM, which is very helpful to decrease computational burden of the RBDO. In addition, a new suitable transfer function that enhanced convergence rate and search ability of the original BBA is introduced.