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This study aimed to develop a new adaptive artificial bee colony (A-ABC) algorithm that can adaptively select an appropriate search equation to more accurately estimate transport energy demand (TED). Also, A-ABC and canonical artificial bee colony (C-ABC) algorithms were compared in terms of efficiency and performance. The input parameters used in the proposed TED model were the official economic indicators of Turkey, including gross domestic product (GDP), population, and total vehicle kilometer per year (TKM). Three mathematical models, linear (A-ABCL), exponential (A-ABCE), and quadratic (A-ABCQ) were developed and tested. Also, economic variables were generated using the "curve fitting" technique to see TED's projections for the year 2034, under two different scenarios. In the first scenario, the results of linear, exponential, and quadratic models according to 2034 TED estimates were 40.1, 31.6, and 70.5 million tons of oil equivalent (Mtoe), respectively. In the second scenario, the results of linear, exponential, and quadratic models according to the TED estimates for 2034 were found as 40.0, 31.5, and 66.5 Mtoe, respectively. The presented models, A-ABCL, A-ABCE, A-ABCQ for the solution of the TED problem, produced successful results compared to the studies in the literature. Besides that, according to global error metrics, developed models generated lower error values than C-ABC. Furthermore, consumption estimation values of A-ABCL and A-ABCE were lower than A-ABCQ. According to A-ABCQ model estimations for both scenarios, the TED value would increase approximately three times from 2013 to 2034. [ABSTRACT FROM AUTHOR]

Residual drift of structural systems after seismic events can make buildings uninhabitable and may cause their collapse in aftershocks, as seen in the 2023 Turkey-Syria earthquake. Self-centering rocking-core systems can eliminate plastic deformation of structures after earthquakes. In the current study, a bi-rocking steel-braced frame was designed using the modified modal superposition (MMS) method. The optimal location of the secondary joint was found to minimize the base shear, overturning moment, peak floor acceleration and inter-story drift. Damage to non-structural components has been considered. The models included 12-, 18- and 24- story frames that were subjected to far-field (FF), near-field-pulse (NP) and near-field-no-pulse (N) ground motion records. The seismic records were scaled to the maximum credible earthquake level. Nonlinear time-history analyses were performed using OpenSees open-source finite element software. Additionally, post-tensioned cables and energy dissipation fuses as variable parameters were optimized using particle swarm optimization. The performance of seven metaheuristic algorithms was compared and the results showed that placing the secondary joint at the mid-height of the structure reduced all seismic demand. The results also suggest that the story moment and FF records were most sensitive to the location of the secondary joint. It was observed that frames with lower stiffness and yield strength of the damper exhibited better seismic performance. Based on this, corrective percentages have been proposed for the design of the cable and dampers. A self-centering ratio of 1.07 was suggested at the mid-height and 1.19 for the base damper. The colliding bodies optimization algorithm performed best for time-history analysis-based optimization. • Using a bi-rocking mechanism to reduce the higher mode effects of high-rise structures. • Designing the bi-rocking steel-braced frame with modified modal superposition method. • Optimization of the main design parameters of bi-rocking joints using metaheuristic algorithms. • Investigating the performance of the bi-rocking steel-braced frame under far- and near-field ground motions. [ABSTRACT FROM AUTHOR]

This study proposed a metaheuristic pansharpening (MP) method, which performs in a Synthetic Variable Ratio (SVR)-like manner. The proposed method introduced the Symbiotic Organisms Search (SOS) algorithm, an advanced nature-based optimization algorithm, to estimate a weight for each multispectral (MS) band to achieve the optimum intensity. The SVR pansharpening formula was used as the objective function and the Root Mean Square Error (RMSE) metric was used as the fitness function of the SOS algorithm to optimize the intensity. The spectral and spatial quality of the results of the MP method were qualitatively and quantitatively compared against those of 15 widely-used pansharpening methods in 5 test sites in Turkey with different land cover features. The experiments aimed to spatially enhance WorldView-2 MS images by using WorldView-2 panchromatic (PAN) bands and a UAV-derived PAN orthophoto. It was also aimed to sharpen IKONOS MS images by using a QuickBird pansharpened image and an IKONOS PAN band. The MATLAB software was used to implement the proposed method and to compute the spatial and spectral quality metrics. The spatial quality of each pansharpened image was evaluated at full-scale, whereas the spectral quality of each pansharpened image was evaluated at both full-scale and reduced-scale. A scoring strategy based on giving a performance score with respect to the spatial and spectral quality metrics was used to ensure a fair comparison among the pansharpening methods used. The results demonstrated that, out of 16 pansharpening methods, the MP method achieved the highest overall spectral and spatial quality scores of 15.5 and 15.5, respectively. The proposed method was found to perform successfully with both singlesensor and multisensor input images. It was also concluded that the proposed method is able to deal with high spatial resolution ratio between the input images. [ABSTRACT FROM AUTHOR]

Modeling and optimization from natural phenomena and observations of the physical earth is an extremely important issue. In the light of the developments in computer and artificial intelligence technologies, the applications of learning-based modeling and optimization techniques in all kinds of study fields are increasing. In this research, the applicability of four different state-of-the-art metaheuristic algorithms which are Particle swarm optimization (PSO), Tree-Seed Algorithm (TSA), Artificial Bee Colony (ABC) algorithm, and Grey Wolf Optimizer (GWO), in local GNSS/leveling geoid studies have been examined. The most suitable geoid model has been tried to be obtained by using different reference points via the well-known machine learning algorithms, Artificial Neural Network (ANN) and Extreme Learning Machine (ELM), at the existing GNSS/leveling points in Burdur city of Turkey. In this study, eight different hybrid approaches are proposed by using each metaheuristic algorithm together with machine learning methods. By using these hybrid approaches, the model closest to the minimum number of reference points has been tried to be obtained. Furthermore, the performance of the hybrid approaches has been compared. According to the comparisons, the hybrid approach performed with GWO and ELM has achieved better results than other proposed hybrid approaches. As a result of the research, it has been seen that the most suitable local GNSS/Leveling geoid can be determined with a lower number of reference points in an appropriate distribution. • A new problem for reducing the GNSS/leveling geoid points is introduced. • Eight different hybrid approaches are used in the solution of the related problem. • 102 measured reference points in Burdur, Turkey are used. • By the GWO+ELM approach, 82 reference points are reduced to 8 reference points with almost the same accuracy. • The cost and time taken to measure the reference points may reduce with the proposed method. [ABSTRACT FROM AUTHOR]