Engineering Science and Technology-An International Journal-Jestech最新文献

In Turkey, one of the most important earthquake belts in Europe, two major earthquakes (Mw: 7.7 and 7.6) occurred about 8 h apart in Kahramanmaraş province on February 6, 2023. Located on the South Anatolian Fault Zone (EAFZ), Kahramanmaraş province and 10 surrounding provinces were affected by the earthquake, which killed approximately 50,000 people and injured approximately 250,000 people. When the cause of this effect was analyzed, it was determined that the building stock of the region was old, as well as the peak ground acceleration (PGA) values that occurred on the surface and to which the structures were exposed. In addition to structural problems, it has been determined that there are significant ground problems such as ground amplification and ground liquefaction in the geotechnical field. For this reason, it is necessary to accurately determine the spectral acceleration values, which show important parameters in the design of buildings, and to take necessary precautions by identifying problems such as soil bearing capacity, settlement problem, liquefaction, and soil amplification in advance. Turkey Building Earthquake Code (TEC 2019) entered into force in 2019. In this study, surface acceleration records exposed to the structures in the region were analyzed. Earthquake acceleration records were obtained from the stations closest to the epicenters of the earthquakes that occurred in Elbistan and Pazarcık in Kahramanmaraş province, and soil amplification analysis and liquefaction potential risk analysis were performed at 3 different points in Kahramanmaraş province. In addition, 11 earthquake acceleration records were selected from different parts of the world reflecting the earthquake characteristics of the region and the analysis was repeated and the design spectrum was compared with the data obtained from the field. Site-specific soil behavior analyses were performed in the study where the existing conditions of the structures in the region were also examined. The data obtained show that the regulation does not fully reflect the field in cases where the structures are exposed to high acceleration values on the surface due to the soil structure, at the same time, liquefaction problems are high and the structures have not undergone the necessary ground improvement processes.

It is well known that road safety is a major problem in cities, resulting in a large number of accidents with significant injuries and loss of life. Much of this problem occurs when vehicles interact with pedestrians. To try to minimize this problem to a large extent, a combined system using resins and a photoluminescent additive was proposed. To confirm the goodness of this material, a characterisation was carried out covering luminance, vibroacoustic and mechanical properties and a study of its photogrammetry under real conditions of use. A luminance of 68 mcd/m² at 20 min was confirmed, which would allow, by a wide margin, a pedestrian crossing to be observed in a vehicle more than 100 m away. The acoustic vibration test confirmed that the proposed system would provide a very efficient audible warning to pedestrians and would reduce the average vehicle speed by about 37 % overall, while in cases where vehicles have to stop for pedestrians, this reduction would be about 28 %. With the mechanical characterisation, it was possible to determine a vertical displacement of always less than 2 mm in vehicles with a wheel load of 12.5 kN, reaching a compressive and tensile strength of more than 56 MPa. The results obtained confirm a potential reduction in mortality of close to 110 %, and injuries by approximately 55 %, as a consequence of the reduction in vehicle speed. In addition, improved night-time visibility of pedestrian crossings would reduce deaths by 35 % and injuries by 26 %, while in the most favourable situations, these values would be 14 % and 10 % for deaths and injuries respectively. All this confirms the great advantage of the system for improving road safety in urban environments.

Experts conduct damage assessments throughout the city in earthquake-prone areas to evaluate the destruction caused by the earthquake. Based on the ATC-20 Building Safety Values, the buildings impacted by the earthquake are categorized as “Inspected, Restricted Use, Unsafe”. Visual imagery captured both inside and outside the buildings is utilized to document the expedited identification of structural deficiencies and their underlying causes. Nevertheless, architects and engineers find the documentation, reporting, and decision-making process to be a time-consuming task. In the past ten years, extensive research has been carried out to reduce the duration of these procedures, specifically in the fields of construction and machine learning. This study investigates the application of machine learning in decision support systems, drawing on research on post-earthquake damage assessment. Post-earthquake damage assessment reports utilized CNN damage assessment algorithms to classify exterior images of buildings exhibiting “Inspected, Restricted Use, Unsafe” damage. The accuracy and loss values of various algorithms, including different AlexNet algorithms, the VGG19 algorithm, and the Resnet50 algorithm, were compared.

Optimizing speed and propulsive efficiency are the most crucial survival skills for biomimetic robots. This paper investigates a swimming mode controller inspired by the black Knifefish to govern the fast-swimming gait with high propulsive efficiency for an elongated undulating fin robot. The proposed swimming mode controller is composed of a couple of Hopf oscillator-based central pattern generators (CPG) to generate the moving gait of robotic fish and a novel variant of Reinforcement Learning (RL) known as Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) for optimizing the propulsive efficiency. The proposed swimming controller facilitates the autonomous optimization of the oscillatory amplitude of the robotic fish to improve its propulsive efficiency. The proposed MA-DDPG demonstrates an aptitude for functioning within mixed cooperative-competitive environments. Furthermore, it effectively mitigates the drawback of zero amplitude in the updating process of conventional reinforcement learning (RL) methodologies. These findings highlight the potential utility of the MA-DDPG in optimizing the performance of multi-agent systems in dynamic, real-world scenarios. The simulation results show that the undulating fin robot reaches a maximum thrust of 0.9 N with a propulsive efficiency of 12.48 %, which is higher than that of traditional reinforcement learning methods.

This paper proposed two evaluation metrics of the tamper coincidence in a block map design for image watermarking. These evaluation metrics are called Tamper Coincidence Block Ratio (TCBR) and Tamper Coincidence Block Density (TCBD). A tamper coincidence occurred in image authentication and self-recovery when the recovery data and the original block location were tampered with simultaneously. A high tamper coincidence limits image inpainting’s capability to recover the region, leading to an imprecise recovered image. The ratio and density of the tamper coincidence may significantly affect the final recovered image quality. Previously, researchers mentioned the tamper coincidence in their experiment but did not evaluate it with any metrics. They evaluated the robustness of their technique based on the final recovered image quality using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Tamper coincidences are primarily affected by the block map design implemented by the researcher. Thus, TCBR and TCBD provide valuable insight into the block map design’s effectiveness in preventing tamper coincidence. The experimental result shows that the TCBR and TCBD values are inversely proportional to the recovered image quality. A high TCBR and TCBD value leads to low recovered image quality. Therefore, this paper will help the researchers design an effective block map by minimizing the TCBR and TCBD values to obtain the highest recovered image quality.

The advancement and broad application of 3D printing technology in industries such as aerospace, healthcare, and manufacturing highlight the critical need for optimizing printing processes to achieve superior mechanical properties and overall performance of printed objects. Despite the progress in 3D printing, achieving optimal mechanical properties remains challenging due to a lack of systematic parameter selection and understanding of parameter interactions. So, to overcome these drawbacks, this research focuses on the systematic optimization of mechanical properties through precise parameter selection and experimental analysis. Aramid fiber-reinforced Polyethylene Terephthalate Glycol (PETG-KF) is used as the printing material on an X1E Fused Filament Fabrication (FFF) 3D printer. Key printing parameters such as orientation, printing speed, layer height, and infill density are carefully chosen to explore their impact on mechanical properties. An L16-Orthogonal Array (L16-OA) is employed to systematically investigate various combinations of these parameters. The experiments are conducted using the FFF-3D printer, and the mechanical properties, including Ultimate Tensile Strength (UTS), hardness, Fatigue Resistance (FR), and Impact Strength (IS), are evaluated using a Universal Testing Machine (UTM). Data analysis incorporates Backpropagation Neural Network (BPNN) modeling for understanding non-linear relationships between input parameters and mechanical properties, alongside Analysis of Variance (ANOVA) to assess parameter significance. Further, a confirmation run, validates the optimized parameters, ensuring their practical applicability. This research offers a structured methodology to enhance the mechanical performance of 3D-printed objects, contributing valuable insights to the additive manufacturing field. In addition, the experimental UTS (E-UTS), experimental hardness (E-Hardness), experimental IS (E-IS), experimental FR (E-FR) are measured and compared with predicted UTS (P-UTS), predicted hardness (P-Hardness), predicted IS (P-IS), predicted FR (P-FR), which are estimated by BPNN model. Finally, the research concludes by comparing experimental and predicted mechanical properties and analyzing Relative Errors (RE) to identify the most effective parameter combinations for the L16-OA.

In the domain of data-centric networks, Link Prediction (LP) is instrumental in discerning potential or absent connections among entities within complex networks. By employing graph data structures, LP techniques enable a detailed analysis of entity interactions across varied sectors, contributing significantly to overcoming challenges in data filtering and integrity restoration, primarily when the network does not provide embedded data. Although LP methods are widely applicable, especially in recommender systems, their efficacy in current social networks needs to be thoroughly investigated. This study introduces an innovative LP approach using Deep Neural Networks (DNNs). We compare our method against a comprehensive set of established techniques, including traditional score-based methods, classical baselines, and recent deep learning approaches like Graph Neural Networks (GNNs). Our DNN-based solution incorporates a robust feature extraction process and a binary classifier, optimized for accurate prediction of missing links within networks. We performed extensive experimental evaluations on diverse datasets, including co-authorship networks, e-commerce, and social media networks. The study encompasses a comparative analysis with traditional LP techniques, namely Common Neighbors, Resource Allocation Index, Jaccard’s Coefficient, and Adamic/Adar Index, as well as other selected baseline and deep-learning methods. Our findings demonstrate that the DNN-based approach significantly enhances predictive accuracy, outperforming the conventional baseline methods in link prediction.

Index modulation (IM) techniques are among the competitive candidates for fifth-generation and beyond (5GB) systems, offering new ways of conveying information thanks to their advantages such as structure flexibility and hardware convenience. Meanwhile, research on orthogonal frequency division multiplexing (OFDM) performance improvements for next-generation wireless communication systems is still intensively ongoing. Accordingly, the IM system has been adapted to OFDM, which allows additional bits of information to be transmitted through the subcarrier indices of the OFDM. Nevertheless, hardware impairments (HWIs) limit the performance of the transceiver. In the literature, reconfigurable intelligent surface (RIS) technology controls the propagation environment and enhances the quality of the received signal by modifying the phase of the incoming signal. In this paper, we investigate the effects of in-phase (I) and quadrature-phase (Q) imbalance (IQI) on RIS-based OFDM-IM transceivers motivated by the benefits of the RISs. Firstly, we present an RIS-assisted OFDM-IM model subject to transmitter and receiver IQI effects. Next, the average bit error rate (ABER) performance of the RIS-assisted OFDM-IM is calculated by the provided mathematical expressions taking the effect of IQI into account. The simulation outputs show that the designed RIS-supported scheme achieves a performance improvement compared to the traditional OFDM-IM under the effect of IQI.

Many cities in Türkiye have been affected by the devastating twin earthquakes in Kahramanmaraş. Following the Kahramanmaraş earthquakes, a field study was conducted to investigate the material characteristics of concrete used in collapsed buildings. The study’s major aim is to examine several parameters of reinforced concrete structures by comparing the samples taken from different locations in the region after the Kahramanmaraş Earthquakes for post-earthquake material characterization. Investigating the effect of concrete damage in these earthquakes, which especially affected 11 provinces and an area wider than approximately 100 thousand square kilometers, is of greater importance. For this purpose, Schmidt hammer measurements were carried out on structural elements, and concrete samples were collected from the same structures. Compression tests were conducted on the collected 21 samples taken from the 25 residential buildings to determine the mechanical properties of concrete such as compressive strength, modulus of elasticity, and Poisson’s ratio. Also, microstructural investigations were conducted on the concrete samples. The results showed that concrete samples had generally low strength and low deformation ability causing brittle failure of structures. When the compressive strengths of the concrete samples taken by the core drilling method were examined, it was determined that the results were between 4 MPa and 41 MPa, while the average compressive strength result was 16.7 MPa and the standard deviation was 8.6 MPa. Additionally, compressive strength results over 20 MPa were determined in 5 samples. The elasticity modules of the samples with compressive strength results above 15 MPa are higher than the value found in the equation given in TS500, while the others are lower. The elasticity modulus results were more compatible with the equation given in Eurocode. In addition, although there is no full agreement between the results obtained from Schmidt hammer measurements and the core drilling method results, the non-destructive method helps to find the weakest element on any floor. It can be concluded that there is a connection between the quality of concrete and the performance of buildings under severe earthquakes. Therefore, it is recommended to improve building codes, strengthen the inspection procedures, detail the quality control mechanisms, and sustain education/training programs to increase the resilience of the buildings in future earthquakes.