Arabian Journal for Science and Engineering最新文献

To achieve a controllable carrying and deformation capacity without needing post-earthquake replacement, this study introduces a novel design for buckling-restrained braces, leveraging friction energy dissipation. The braces comprise four integral components: an inner steel tube, a high-strength compression spring, a friction plate, and an outer steel tube. An axial cyclic loading test conducted on three distinct sets of specimens with varied components investigates the carrying capacity, deformation capacity, and energy dissipation capacity of the buckling-restrained braces. Furthermore, an analysis is performed to assess the influence of the high-strength compression spring and friction plate material on the overall performance of the buckling-restrained braces. The test results demonstrate that, in comparison with the traditional buckling restrained brace, the friction buckling restrained brace exhibits the following advantages: (1) The hysteresis curve of the friction dissipative buckling restrained brace exhibits superior coverage compared to that of the traditional buckling restrained brace; (2) the FBRB demonstrates enhanced load-carrying, deformation, and energy dissipation capabilities compared to the BRB; and (3) the FBRB exhibits a distinctive axial adjustment capacity due to the incorporation of spring members, which can extend the service life of the member. The findings indicate that this type of buckling-restrained brace exhibits an adjustable carrying and deformation capacity, a complete hysteretic curve, and no buckling of the core member under compression. The application of these braces proves effective in significantly reducing the cost of structural rehabilitation post-earthquake.

This paper presents an ultra-low-voltage 10-bit successive approximation-register analog-to-digital converter (SAR ADC) based on the binary search algorithm for biomedical applications. An energy-efficient DAC switching scheme for a fully differential SAR ADC is proposed, which achieves a 99.8% reduction in DAC switching energy compared to conventional SAR ADC. In this design, by using a dummy capacitor split technique, an attempt has been made to reduce the capacitor of the most significant bit, resulting in a 92.87% reduction in the total number of capacitors compared to conventional design. In the proposed structure, the common-mode voltage of the comparator is approximately constant. The maximum voltage variation in the proposed switching scheme is Vref/2. Additionally, power consumption has been reduced by implementing the power gating technique in the control logic part. The proposed converter with a sampling frequency of 1 kS/s and a supply voltage of 0.2 V has been designed and simulated in TSMC 65nm CMOS technology. Both analytical calculations and simulation results confirm the effectiveness of the proposed switching scheme. Ultimately, the proposed scheme achieves a power consumption of 2.08 nW and a Figure of Merit (FoM) of 5.39 fJ/conversion-step. In comparison with the state-of-the-art, the proposed design has demonstrated excellent performance in achieving optimal power.

Faulting predictive models are crucial for maintaining the structural integrity and safety of rigid pavements, ensuring a smooth and durable driving surface. Accurate predictions allow for timely maintenance, reducing long-term costs and extending pavement lifespan. The objective of this study is to advance faulting prediction methodologies for jointed reinforced concrete pavement (JRCP) to bolster pavement longevity and maintenance strategies. Using data from 22 distinct sections under the long-term pavement performance (LTPP) program, encompassing a wide array of climatic scenarios, the research leverages six cutting-edge machine learning algorithms: regression tree (RT), support vector machine (SVM), ensembles, Gaussian process regression (GPR), artificial neural network (ANN), and kernel methods. The methodology includes a detailed statistical analysis and an evaluation of feature significance to dissect the multifaceted interactions among key determinants of pavement performance. The results underscore the efficacy of machine learning in elevating faulting prediction precision. Among the algorithms tested, boosted trees demonstrated the highest accuracy, with a root mean square error (RMSE) of 0.68, a mean squared error (MSE) of 0.46, and an R-squared value of 0.78. The feature importance analysis highlighted that L4 Thickness, pavement age, L3 Type, and initial IRI were the most influential factors in predicting faulting, with importance scores of 0.2266, 0.1862, 0.1638, and 0.1594, respectively. This study demonstrates the significant potential of machine learning models in accurately predicting faulting in JRCP, paving the way for more efficient pavement maintenance and management strategies that can effectively address and mitigate pavement distress.

Machining CFRP with WEDM is extremely challenging and produces kerf of poor quality. Therefore, the present research venture is intended to improve the kerf quality produced in WEDM of woven CFRP through a machine learning-based metaheuristic algorithm. Two ensemble-based machine learning algorithms i.e., the Random Forest (RF), and Adaptive Boosting algorithm (AdaBoost) have been used to model the kerf width. The performance of RF is found to be superior to AdaBoost in terms of generalization prowess as the box plot corresponding to the predicted KW by RF closely resembles the box plot of experimental KW whereas the box plot corresponding to the predicted KW by AdaBoost has a varying distribution with the box-plot of experimental KW. Furthermore, the kerf width optimization has been conducted using a broad range of optimization techniques from nature-inspired to mathematically driven approaches such as the Moth flame optimizer (MFO), Grey Wolf optimizer, Chimp optimization algorithm, and sine cosine algorithm in an attempt to compare the computational performance of the algorithms. It has been revealed that MFO discovered the minimum KW (global optimum solution) and exhibited rapid convergence as compared to its counterparts. The optimal results are Ton = 26 microsecs, Toff = 50 microsecs, I = 7A, and V = 70 V. Additionally, the proposed optimization's durability has been examined using the traditional desirability approach. The percentage improvement in KW through the proposed optimization as compared to the desirability approach is 5.6%. Lastly, FESEM images are provided for varying process parametric conditions.

Accurately identifying pertinent text segments as answers to questions is crucial for optimizing question-answering systems, underscoring the pivotal role of precision in Answer Sentence Selection (AS2) modules. This study introduces an innovative AS2 module design leveraging the AraBERT transformer to encode inputs-one for the question and one for the candidate answer-with the goal of enhancing comprehension of both inputs. Each encoded input is subsequently processed in parallel by a collaborative layer employing two distinct deep learning models: a bidirectional long short-term memory (BiLSTM) and a convolutional neural network (CNN). This collaborative approach forms the AraBERT.Collab-BiLSTM/CNN model. Additionally, extensions to the study include AraBERT.Collab-BiLSTM/AVG, incorporating a BiLSTM and AVG collaboration layer, as well as the use of the AraELECTRA pre-trained model, yielding the AraELECTRA.Collab-BiLSTM/CNN and AraELECTRA.Collab-BiLSTM/AVG configurations. Furthermore, the study investigates Arabic word embedding models as alternatives to pre-trained models, resulting in the WordEmb.Collab-BiLSTM/CNN and WordEmb.Collab-BiLSTM/AVG models. Experimental results on our BARAQA (Big-ARAbic-Question-Answering) dataset and the SemEval Arabic Question-Answering corpus demonstrate that the AraELECTRA.Collab-BiLSTM/CNN model achieves high accuracies of 84.64% and 45.93%, respectively. Moreover, the WordEmb.Collab-BiLSTM/AVG model significantly enhances accuracy to 91.61% and 81.23% on the respective datasets, showcasing the effectiveness of our collaborative techniques. Our proposed architecture represents a substantial improvement over previous models, emphasizing the importance of advanced techniques and collaborative strategies in handling complex language structures and diverse text dependencies. Additionally, the study underscores the performance of Arabic transformer-based encoding and suggests further exploration of transformers and collaborative strategies to bolster AS2 performance.

The purpose of this study was to develop a self-cleaning and antireflective coating for commercial solar panels using low surface energy materials such as PVDF (Polyvinylidene fluoride), PDMS (Polydimethylsiloxane), and TiO₂ as an antireflective agent. This work addressed the significant impact of environmental dust deposition on solar panel efficiency and maintenance challenges. The coated glass substrates were analyzed through contact angle measurements, transmittance assessments, SEM, and AFM. It was found that hydrophobicity and transmittance were influenced by the formulation's ingredient composition. Results demonstrated that a formulation containing 0.5% PVDF, 0.5% PDMS, and TiO₂ achieved a maximum contact angle of 113.75° and highest transmittance. Stability under UV-light exposure was evaluated over 288 h, revealing sustained performance. This research underscored the innovation in enhancing solar panel efficiency and longevity through advanced coating technologies.

This study explores the untapped potential of utilizing heat-treated recycled fines (HT-RFs) from mortar as a sustainable and eco-friendly alternative for slag-based geopolymer materials in construction. The RFs undergo a novel heat treatment process at 650 °C to significantly enhance their reactivity. Geopolymer mixtures incorporating both heat-treated and untreated RFs at various replacement ratios (0%, 10%, 20%, and 30%) are meticulously evaluated for their porosity, flexural strength, and compressive strength after 28 days of curing. The load-midspan displacement and failure behavior are analyzed using digital image correlation techniques. The microstructure of the mortar samples is comprehensively analyzed using state-of-the-art techniques including thermogravimetry, scanning electron microscopy, and X-ray diffraction. The results showed that the incorporation of 30% heat-treated recycled fines into slag based geopolymer increased compressive and flexural strengths by 30.67% and 27.31%, respectively, while substantially reducing the porosity by 16%, compared to the control geopolymer mixture. This study promotes sustainability in construction with eco-friendly materials by minimizing waste.

Urban road safety is severely threatened by the presence and development of subsurface voids, posing a significant challenge. This study proposes a new approach to tackle the challenge of detecting hidden hollows in urban areas. The approach utilizes geophysical investigations using soil stress waves excited by subway tunnel vibrations to examine void defects in the soil layers above. A shaker excited concrete plates to generate the necessary stress waves required for detecting vacancy defects. The results showed that the amplitude of the stress wave signals in both the time and frequency domain significantly attenuated after passing through void defects. There were significant differences in wave impedance at the boundaries of these voids. Wave field propagation contour maps, obtained using laser Doppler vibrometers (LDVs), visually demonstrated the propagation of soil stress waves at the boundaries of void defects. Reflected waves exhibit increased strength with larger void dimensions, while diffracted waves at the soil’s end weaken. The energy distribution contour maps in the time domain provided an intuitive indication of the presence, horizontal projection position, and size of the void defects. Sensitivity coefficients ((omega)), statistical feature indicators such as root-mean-square deviation coefficients ((RMSD)), and wavelet packet energy ratio deviation coefficients ((E_{RVD})) were established to quantitatively characterize the size of void defects from different perspectives. The preliminary findings of this study have verified the feasibility and scientific validity of utilizing soil stress waves generated by subway vibrations for the detection of void defects.

The behavior of jet grouting columns is influenced by several operational and geotechnical parameters, but relatively little attention has been paid to the significance of the initial soil moisture content on the columns. In the current study, a small-scale jet grouting device has been used to create 47 small-scale columns in dry and wet (20% moisture content) sand to investigate the effects of the soil moisture content on the diameter, uniaxial compressive strength (UCS), and the treatment efficiency. The columns were constructed using different injection times, rotational speeds of 10 and 25 rpm, and soil relative densities of 5% and 95% to assess the comparative influence of the moisture content on the effectiveness of other parameters. The results demonstrated that the initial soil moisture had the most significant effect on the column diameter. In sand with a 20% moisture content, the column diameter increased an average of 63% and 87% in loose and dense sand, respectively, compared to dry sand. However, an increase in the injection time in wet sand caused a significant decrease in the treatment efficiency. A 60% decrease in the rotational speed from 25 to 10 rpm had a greater effect on increasing the diameter in dry sand than doubling the injection time. The relative density had a much smaller effect on the column diameter created in wet sand than in dry sand. The high cement-to-soil ratio of the columns increased the UCS of the core samples and eliminated the effect of the aforementioned parameters on the UCS.

This paper presents the results of a detailed experimental investigation into the wear properties of laser powder bed fusion samples of Inconel 718 after various aging conditions. There is a gap in the literature on the high-temperature wear properties of Inconel 718. The aging process determines the service life and working conditions of the alloy. This study aims to reveal the effects of various aging heat treatments on the room and high-temperature wear properties of Inconel 718. The aging conditions were selected as non-aged, solution aged (SA), conventionally aged (CA), overaged (OA) and furnace-controlled aged (FCA). Cylindrical samples were machined for 500 m, using a Si₃N₄ ball at room temperature and 400 °C. The surfaces of the samples were smoothed by turning. FCA was performed in a controlled atmosphere furnace. The heating and cooling rates of FCA were 10 min/°C. Ar was selected as the shielding gas. The rapid cooling stages of SA, CA and OA were performed by quenching in water. FCA refined the microstructure and enhanced the wear resistance. SA resulted in a rigid microstructure, abrasive wear was dominant. EA led to an increase in the Laves phase ratio, which was identified by X-ray diffraction analyses. Optical microscope and scanning electron microscope (SEM) images of the microstructures and worn surfaces were correlated with the microhardness scores to accurately define the wear properties. The precipitations were identified by energy-dispersive X-ray spectrum application that is combined to SEM. The experimental evidence from this work clarified the predominant wear mechanisms due to microstructure and phase evolution. This work provides remarkable information on determining the appropriate aging condition for various applications of Inconel 718 alloy.