One of the most important factors for successful agricultural production is the irrigation system in place. In this study, a precision irrigation system, which takes advantage of the various phases of plant growth, was developed and implemented using the sensor network technology integrated with IOS/Android. The amount of water in the soil was measured via sensors that were placed on certain points of the area to be irrigated. These sensors were placed near the root of the product. Data from sensors was transmitted via Wi-Fi in real-time to a mobile phone based on IOS/Android. In the light of obtained data, the seasonal precision irrigation system was created depending on the amount of water required by the plants at each stage of their growth stage. The required energy of the system was provided by solar energy. The system can be controlled by smart phones, which increases the usability of the system. When design performance was analyzed, it was observed that some important advantages such as obtaining high efficiency with water, time and energy saving and reducing the workforce were ensured. Five separate laterals were used for the irrigation system. There were valves on each lateral, which realized the opening and closing process depending on the water need. A total of 16 humidity sensors were used in the irrigation system and the data from these sensors was transferred to the IOS/Android server via the programmable controller (PLC). The basic electrical equipment in the irrigation system was monitored and controlled via mobile devices. Control parameters were obtained by comparing the real values and reference values by a closed-loop system and determine the new working status of the irrigation system.

Determining the risk priorities for the building stock in highly seismic-prone regions and making the final decisions about the buildings is one of the essential precautionary measures that needs to be taken before the earthquake. This study aims to develop an Artificial Neural Network (ANN)-based model to predict risk priorities for reinforced-concrete (RC) buildings that constitute a large part of the existing building stock. For this purpose, the network parameters in the network structure have been optimized by establishing a hybrid structure with the Genetic Algorithm (GA). As a result, the ANN model can make accurate predictions with maximum efficiency. The suggested ANN model is a feedforward back-propagation network model. It aims to predict the risk priorities for 329 RC buildings in the most successful way, for which the performance score was calculated using the Turkey Rapid Evaluation Method (2013). In this paper, a GA-ANN hybrid model was implemented in which the ANN, using the most successful gene revealed by the model, produced successful results in calculating the performance score. In addition, the required input parameters for obtaining more efficient results in solving such a problem and the parameters that need to be used in establishing such an ANN network structure have been optimized. With the help of such a model, the operation process will be eliminated. The created hybrid model was 98% successful in determining the risk priority in RC buildings.

Minarets are slender and tall structures that are built from different types of materials. Modern materials are also starting to be used in such structures with the recent developments in material technology. The seismic vulnerability and dynamic behavior of minarets can vary, depending on the material characteristics. Within this study’s scope, thirteen different material types used in minarets in Türkiye were chosen as variables. A sample minaret model was chosen as an example with nine different heights to reveal how material characteristic change affects seismic and dynamic behavior. Information and mechanical characteristics were given for all the material types. Natural fundamental periods, displacements, and base shear forces were attained from structural analyses for each selected material. The empirical period formula for each material is proposed using the obtained periods, depending on the different minaret heights taken into consideration. At the same time, fundamental natural periods for the first ten modes and 13 different types of materials used in the study were estimated with the established Artificial Neural Network (ANN) model. The real periods from the experimental analyses were compared with the values estimated by the ANN using fewer parameters, and 99% of the results were successful. In addition, time history analyses were used to evaluate the seismic performance of the minaret (three different materials were considered). In this specific case, the acceleration record from the 2011 Van (Eastern Turkiye) earthquake (Mw = 7.2) was taken into consideration. Performance levels were determined for the minaret according to the results obtained for each material. It has been concluded that material characteristics significantly affect the dynamic and seismic behavior of the minarets.

The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, target displacements were obtained by performing pushover analysis for a sample reinforced-concrete building model, taking into account 60 different peak ground accelerations for each of the five different stories. Three different target displacements were obtained for damage estimation, such as damage limitation (DL), significant damage (SD), and near collapse (NC), obtained for each peak ground acceleration for five different numbers of stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model to predict target displacements under different seismic risks for mid-rise regular reinforced-concrete buildings, which make up a large part of the existing building stock, using all the data obtained. For this purpose, a hybrid structure was established with the particle swarm optimization algorithm (PSO), and the network structure’s hyper parameters were optimized. Three different hybrid models were created in order to predict the target displacements most successfully. It was found that the ANN established with particles with the best position revealed by the hybrid models produced successful results in the calculation of the performance score. The created hybrid models produced 99% successful results in DL estimation, 99% in SD estimation, and 99% in NC estimation in determining target displacements in mid-rise regular reinforced-concrete buildings. The hybrid model also revealed which parameters should be used in ANN for estimating target displacements under different seismic risks.

In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies.

The control of mechanical systems are generally applied by using electric motors which are useful for the control of velocity, position and torque. On the other hand, servo motors are preferred due to their characteristic properties for the systems that require more accurate operations. In this study, a mechanical prototype having a two degree of freedom motion capacity integrated with AC servo motors is generated. The control of those AC servo motors is achieved by using an Omron PLC (Programmable Logic Controller). A SCADA program is developed for user control and monitoring. The connection between SCADA and PLC is obtained by using Device-Net web protocol. The velocity and position of the motion control system are monitored with real time animation on the screen. It is observed after the testing of the designed system that the position and velocity values are convenient.