Clay brick and granite waste are part of the waste generated by construction and demolition activities. The amount of these wastes generated is enormous, but on the one hand, they can be used as a raw material for cement mixtures; thus, it is important to find ways to utilize them efficiently. In this study, clay brick and granite waste were crushed and screened into two size fractions (0.15–2.36 mm for sand replacement and smaller than 0.15 mm for cement replacement), and a total of four different forms of recycled materials were obtained (recycled brick aggregate, recycled brick powder, recycled granite aggregate and recycled granite powder) and used in cement mortar. Various properties (workability, mechanical strength and drying shrinkage) of the mortars were assessed according to standardized test methods. The results showed that the various material forms had different effects on the various properties of cement mortar. At replacement ratios of 10% and 20%, recycled granite showed better workability when used as powder, whereas recycled brick used as aggregate had higher workability. In common, using recycled brick and recycled granite in the form of aggregate was advantageous for the strength development of mortar, while using them in the form of powder helped to mitigate drying shrinkage.

With the development of various metaheuristic algorithms, research cases that perform weight optimization of truss structures are steadily progressing. In particular, due to the possibility of developing quantum computers, metaheuristic algorithms combined with quantum computation are being developed. In this paper, the QbHS (Quantum based Harmony Search) algorithm was proposed by combining quantum computation and the conventional HS (Harmony Search) algorithms, and the size and topology optimization of the truss structure was performed. The QbHS algorithm has the same repetitive computational structure as the conventional HS algorithm. However, the QbHS algorithm constructed $QHM$ (Quantum Harmony Memory) using the probability of Q-bit and proposed to perform pitch adjusting using the basic state of Q-bit. To perform weight optimization of truss structures using the proposed QbHS algorithm, 20 bar, 24 bar, and 72-bar truss structures were adopted as examples and compared with the results of the QE (Quantum Evolutionary) algorithm. As a result, it was confirmed that the QbHS algorithm had excellent convergence performance by finding a lower weight than the QE algorithm. In addition, by expressing the weight optimization results of the truss structure with an image coordinate system, the topology of the truss structure could be confirmed only by the picture. The results of this study are expected to play an important role in future computer information systems by combining quantum computation and conventional HS algorithms.

The conventional crow search (CS) algorithm is a swarm-based metaheuristic algorithm that has fewer parameters, is easy to apply to problems, and is utilized in various fields. However, it has a disadvantage, as it is easy for it to fall into local minima by relying mainly on exploitation to find approximations. Therefore, in this paper, we propose the advanced crow search (ACS) algorithm, which improves the conventional CS algorithm and solves the global optimization problem. The ACS algorithm has three differences from the conventional CS algorithm. First, we propose using dynamic $AP$ (awareness probability) to perform exploration of the global region for the selection of the initial population. Second, we improved the exploitation performance by introducing a formula that probabilistically selects the best crows instead of randomly selecting them. Third, we improved the exploration phase by adding an equation for local search. The ACS algorithm proposed in this paper has improved exploitation and exploration performance over other metaheuristic algorithms in both unimodal and multimodal benchmark functions, and it found the most optimal solutions in five engineering problems.

This study assesses the impact of hyperparameter optimization algorithms on the performance of machine learning-based concrete compressive strength prediction models. Three datasets were used to compare the performance of a basic model that had not undergone hyperparameter optimization, with the model incorporating random search, grid search, and Bayesian optimization. A post-hoc analysis using Shapley additive explanations was also conducted. The results demonstrate that the effectiveness of hyperparameter optimization varies depending on the characteristics of the dataset. For Dataset 1, the application of search algorithms appeared to improve prediction accuracy to some extent. However, for Datasets 2 and 3, the performance improvement from the search algorithms was either insignificant or decreased. Despite these contrasting results, the post-analysis showed that the influence of each feature generally aligned with empirical knowledge across all datasets, suggesting that the Shapley additive explanations method alone may have limitations in pinpointing the causes of model overfitting.

This study presents a method for the automated design of reinforced concrete (RC) beam cross-sections using an artificial neural network (ANN) trained with cost-optimized data generated by the crow search algorithm (CSA). To effectively employ the CSA, recognized for its benefits in addressing engineering problems among metaheuristic algorithms, the design variables of the RC beam cross-section were mediated. The goal is to improve the design efficiency and prediction accuracy by using data with clear trends derived through metaheuristic optimization. The ANN model is trained with input variables, including the design bending moment and the beam height, and outputs design variables, such as the beam width, number of reinforcement bars, and bar diameters. The model trained with the CSA-optimized data is compared with one trained using randomly generated data. The results show that the CSA-trained model achieves a higher prediction accuracy across all the output variables, with a particularly strong linear relationship for beam width. Additionally, a design scenario demonstrates that the CSA-based model can propose a cross-section with an approximately 17.3% cost reduction compared to the random model. The design based on the CSA methodology demonstrates greater efficiency, as it employs a smaller value for the beam width and requires less reinforcement while still satisfying the flexural requirements. Conversely, the design utilizing the random dataset proves inefficient in terms of both the value for the beam width and the reinforcement layout. A SHAP analysis further confirms that the CSA-based model learns more meaningful structural relationships. The findings emphasize the critical role of high-quality training data in ANN-based structural design and suggest the potential for extending the framework to multi-objective design tasks.

The optimal design of truss structures is one of the most complex problems, as it requires achieving high stiffness and stability while pursuing lightweight structures. With the recent advancements in AI technologies, machine learning-based approaches for predicting the optimal cross-sectional areas of truss structures have garnered significant attention from researchers. However, the design problem of truss structures poses substantial challenges for machine learning models due to the highly diverse and nonlinear characteristics of the optimal cross-sectional distributions, which may hinder effective learning. To address these limitations, the importance of hyperparameter optimization (HPO) has been increasingly recognized. This paper employs metaheuristic algorithms, which are efficient in searching for global optima, to perform HPO on 10-bar and 17-bar truss structure datasets. By balancing exploitation and exploration capabilities, metaheuristic algorithms demonstrate superior performance and time efficiency compared to conventional HPO methods. The results underscore the critical role of hyperparameters in machine learning-based truss structure design and suggest that leveraging metaheuristic algorithm-based HPO holds significant potential for addressing complex structural design problems in future applications.