Engineering reports : open access最新文献

A depth investigation into the impact of high temperatures on the load-bearing capacity of reinforced concrete beams in the case of probabilistic design is presented in this paper, employing advanced finite element analysis techniques. This study addresses a critical knowledge gap in the design of fire-resistant concrete structures, with specific emphasis on the function of concrete cover. The research aims to enhance the overall safety and reliability of concrete buildings under high temperature conditions by providing valuable insights into the behavior of reinforced concrete beams under thermal loading. The analysis incorporates reliability-based modeling to account for uncertainties in temperature distribution within the beams. A validated finite element model is employed to simulate the performance of reinforced concrete beams at elevated temperatures. By considering various concrete cover thicknesses and heat distribution scenarios, the influence of these factors on the load-bearing capacity is thoroughly examined. The results underscore the importance of augmenting the concrete cover to enhance the load-carrying capacity of the beams. Furthermore, the study examines the impact of temperature distribution uncertainties, unveiling diverse load capacities associated with different configurations of concrete cover.

The Beishan Exploration Tunnel (BET) is a facility built to develop technologies associated with the safety of China's first high-level radioactive nuclear waste(HLW) disposal. The surrounding rock discontinuity identification is a key research topic in BET, which could provide essential geological data for future HLW disposal stability and integrity research. This article presents the rock discontinuity identification research progress in BET based on Structure from Motion (SfM) photogrammetry technology. The discontinuity identification algorithm is improved by introducing the region-growing algorithm to optimize the candidate subplane. This algorithm automatically picks the seed, avoids human intervention, and thus increases the work efficiency of the discontinuity identification. The FCM method is improved by embedding with the CFSFDP algorithm in the discontinuity sets grouping. The CFSFDP algorithm coincides well with the Fisher distribution of discontinuity orientations, which is suitable for the Beishan situation. A parallel scheme is used when implementing the method, which accelerates the discontinuity calculation. This improved rock discontinuity identification method was tested on a slope above the BET and applied in the BET. The discontinuity identification results were compared with the results from the manual field measurement and the open-source software DSE. The results show that the improved discontinuity identification method obtains reliable discontinuity data and costs less time and human workload than the other two methods. The surrounding rock discontinuity identification research provides a powerful tool for the Beishan HLW disposal geological investigation.

Instructional videos have the advantage of delivering visual and verbal learning materials simultaneously and they can be used for teaching. The aim of this study is to evaluate the learning outcomes in Mechanical Systems Analysis based on the Finite Element Method for video-based teaching blended with face-to-face education. Forty-five students whose first languages are English, Mandarin and others participated in a survey to evaluate learning outcomes. The learning outcomes were analyzed using the one-sample Chi-square test or binomial test. The correlation between the usefulness of the text-based and video-based learning materials and the first language of the students was analyzed using the Pearson Chi-square test. P values smaller than 0.05 were assumed to be statistically significant. Overall 34 students found video-based learning materials very useful whereas 10 students found them useful resulting in a statistically significant difference. Twenty-four students found text-based learning materials very useful whereas 20 students found them useful without statistically significant difference. Student cohorts speaking English and Mandarin as their first language found video-based learning materials significantly very useful. Video-based learning materials can be used to improve learning outcomes in courses with computer applications and students can benefit from a blended teaching strategy regardless of their first language.

Little is known about the catastrophic bushfire from a micro-pollution point of view, and there is also very limited understanding of the emerging contamination of microplastics and nanoplastics. Upon exposure to fire, plastic items, such as water tanks, may release a substantial quantity of microplastics and nanoplastics, as characterized in this study through the analysis of residual debris. Using Raman imaging with the scanning pixel size down to 100 nm × 100 nm, we over-scan the sample surface to collect a hyperspectral matrix. In order to map and convert the scanning hyperspectral matrix to an image, we compare and advance the chemometrics of algorithms, including logic and principal component analysis (PCA), to extract the weak signal of microplastics and particularly nanoplastics, which enables us to directly visualize the different degrees of burning. By doing so, we can identify the microplastics and nanoplastics down to ˜100 nm, which means that we can break through the diffraction limit of the laser which is ˜296 nm (λ/2NA) to capture nanoplastics. Using statistical analysis, we estimate that 1.4–4.7 million micro- and nanoplastics per cm² can be left behind by the mimicked-bushfire-burned plastic tank. This study suggests that bushfire can accelerate the release of micro- and nanoplastics in the environment. This study not only contributes essential insights into the micro-pollution consequences of fire burning but also underscores the urgency of addressing this understudied aspect to inform environmental conservation strategies and public health measures.

In the present work, hybrid composites made of Al2214 alloy with B₄C and graphite were produced by using a liquid metallurgical process. Al2214 alloy was utilized to create hybrid composites that had 1.5–6 wt% of B₄C particles and a constant 3 wt% of graphite particles. Microstructural analysis using scanning electron microscope (SEM), energy dispersion spectroscopy (EDS), and X-ray diffraction (XRD) was done on the produced composites. The density, hardness, ultimate, yield strength, and elongation as a percentage were carried out using ASTM E8 for tensile and E10 standard for hardness test. The wear behavior of Al2214-B₄C and graphite composites was examined as per ASTM G99 standard using a wear testing device under a variety of loads and rotation speeds. Graphite and boron carbide particles were equally dispersed throughout the Al2214 alloy, according to SEM photographs. Graphite and B₄C particles were detected in the Al2214 alloy by EDS and XRD analyses. The density of Al alloy composites was decreased by adding dual particles to the matrix. The Al2214 alloy's hardness, ultimate strength, yield strength, and wear resistance were all enhanced by the inclusion of dual particles, which increased these properties by 15.4%, 40.4%, and 46.7%, respectively. The presence of hybrid particles in the Al2214 alloy was revealed by EDS and XRD patterns. The density of Al alloy composites was decreased by adding dual particles to the matrix. Tensile force micrographs provided further evidence of the unique fracture behaviors shown by the Al2214 alloy and its composites. In order to examine the wear mechanisms and different morphologies of worn surfaces, scanning electron micrographs were taken.

As a basic building block, the THz wave absorber has intense imaging, sensing, and nondestructive testing applications. There are several methods for tuning THz absorbers, including electricity modulation, light modulation, mechanical tuning, using phase change materials, liquid crystal, flexible materials, MEMS technology, and thermally tuning vanadium dioxide. The choice of tuning method depends on the specific application and the desired performance characteristics of the THz absorber. In this work, we report a theoretical description of mechanically tunable THz absorber based on overlapping periodic arrays of graphene nano-disks. The basis of this work is based on the movement of a dielectric surface covered on both sides with graphene disks. This surface moves on a fixed plane while the distance between these two surfaces is free space. Also, the fixed surface consists of a relatively thick layer of gold at the bottom, dielectric on it, and graphene disk patterns on the dielectric. Now, by moving the movable surface in the horizontal direction, it is possible to adjust the amount of absorption in different frequencies of the terahertz (THz) band. Additionally, an equivalent RLC circuit model is developed and theoretical results match with simulated data. The proposed mechanically tunable THz absorber can be exploited in various emerging applications such as sensing due to its capability of covering all of the THz gap and beyond with multiple absorption peaks.

Sentiment analysis, a method used to classify textual content into positive, negative, or neutral sentiments, is commonly applied to data from social media platforms. Arabic, an official language of the United Nations, presents unique challenges for sentiment analysis due to its complex morphology and dialectal diversity. Compared to English, research on Arabic sentiment analysis is relatively scarce. Transfer learning, which applies the knowledge learned from one domain to another, can address the limitations of training time and computational resources. However, the development of transfer learning for Arabic sentiment analysis is still underdeveloped. In this study, we develop a new hybrid model, RNN-BiLSTM, which merges recurrent neural networks (RNN) and bidirectional long short-term memory (BiLSTM) networks. We used Arabic bidirectional encoder representations from transformers (AraBERT), a state-of-the-art Arabic language pre-trained transformer-based model, to generate word-embedding vectors. The RNN-BiLSTM model integrates the strengths of RNN and BiLSTM, including the ability to learn sequential dependencies and bidirectional context. We trained the RNN-BiLSTM model on the source domain, specifically the Arabic reviews dataset (ARD). The RNN-BiLSTM model outperforms the RNN and BiLSTM models with default parameters, achieving an accuracy of 95.75%. We further applied transfer learning to the RNN-BiLSTM model by fine-tuning its parameters using random search. We compared the performance of the fine-tuned RNN-BiLSTM model with the RNN and BiLSTM models on two target domain datasets: ASTD and Aracust. The results showed that the fine-tuned RNN-BiLSTM model is more effective for transfer learning, achieving an accuracy of 95.44% and 96.19% on the ASTD and Aracust datasets, respectively.

With the rapid development of information technology, new educational models using virtual reality technology have received widespread attention from relevant researchers. In the field of vocational education, vocational colleges and training institutions can effectively mobilize students' learning initiative and improve their learning efficiency by using virtual reality technology. This study details the development process and system evaluation of a bespoke virtual reality system that offers a solution to the issues of uncertainty regarding hazards, high teaching expenses, and spatial constraints inherent in the practical training of elevator maintenance. By establishing a virtual environment that is highly reproducible and designing abundant interaction methods, this system facilitates students in attaining mastery over the structural make-up of elevators, the principles of their operation, and the techniques involved in calibrating elevator governors. The system underwent testing by multiple users, and the satisfaction level of the system was ascertained through a questionnaire study, while the effectiveness of the system was evaluated using independent samples t test for data statistics concerning students' performance. The results of the study indicate that the system gained widespread praise among users, and it notably enhanced the students' learning drive, practical abilities, and on-site adaptability.

Machine learning (ML) has been used in human gait data for appropriate assistive device prediction. However, their uptake in the medical setup still remains low due to their black box nature which restricts clinicians from understanding how they operate. This has led to the exploration of explainable ML. Studies have recommended local interpretable model-agnostic explanation (LIME) because it builds sparse linear models around an individual prediction in its local vicinity hence fast and also because it could be used on any ML model. LIME is however, is not always stable. The research aimed to enhance LIME to attain stability by avoid the sampling step through combining Gaussian mixture model (GMM) sampling. To test performance of the GMM-LIME, supervised ML were recommended because study revealed that their accuracy was above 90% when used on human gait. Neural networks were adopted for GaitRec dataset and Random Forest (RF) was adopted and applied on HugaDB datasets. Maximum accuracies attained were multilayer perceptron (95%) and RF (99%). Graphical results on stability and Jaccard similarity scores were presented for both original LIME and GMM-LIME. Unlike original LIME, GMM-LIME produced not only more accurate and reliable but also consistently stable explanations.

Efficient and accurate acquisition of slope shear strength parameters is the key to slope stability analysis and landslide prevention engineering design. This paper establishes a back analysis method based on uniform design, artificial neural network, and genetic algorithm. It can obtain the shear strength parameters of slopes based on information such as the radius and center coordinates of the slip surface obtained from on-site investigations. This method has been applied to engineering practice. The research results indicate that the stability of the waste dump slope is most sensitive to the response of the internal friction angle of the loose body, followed by cohesion, and least sensitive to the response of the soil volume weight. This method can effectively reduce the number of network training samples and efficiently and quickly determine the initial weights of the BP (abbreviation for back-propagation) neural network. This method can efficiently and quickly conduct back analysis to obtain the shear strength parameters of slopes. Using the obtained shear strength parameters for slope stability calculation, the most dangerous slip surface abscissa error, ordinate error, and slip surface radius error are only 3.59%, 0.95%, and 1.83%. It is recommended to promote the back analysis method of shear strength parameters in engineering practice in the future.