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  • Open Access

    ARTICLE

    Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

    Kolli Ramujee1,*, Pooja Sadula1, Golla Madhu2, Sandeep Kautish3, Abdulaziz S. Almazyad4, Guojiang Xiong5, Ali Wagdy Mohamed6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1455-1486, 2024, DOI:10.32604/cmes.2023.043384

    Abstract Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems. Its attributes as a non-toxic, low-carbon, and economical substitute for conventional cement concrete, coupled with its elevated compressive strength and reduced shrinkage properties, position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure. In this context, this study sets out the task of using machine learning (ML) algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field. To achieve this goal,… More >

  • Open Access

    ARTICLE

    A Multi-Objective Genetic Algorithm Based Load Balancing Strategy for Health Monitoring Systems in Fog-Cloud

    Hayder Makki Shakir, Jaber Karimpour*, Jafar Razmara

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545

    Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource… More >

  • Open Access

    ARTICLE

    A Gel-Based Solidification Technology for Large Fracture Plugging

    Kunjian Wang1, Ruibin He1, Qianhua Liao1, Kun Xu1, Wen Wang1, Kan Chen2,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.3, pp. 563-578, 2024, DOI:10.32604/fdmp.2023.030152

    Abstract Fault fractures usually have large openings and considerable extension. Accordingly, cross-linked gel materials are generally considered more suitable plugging agents than water-based gels because the latter often undergo contamination via formation water, which prevents them from being effective over long times. Hence, in this study, a set of oil-based composite gels based on waste grease and epoxy resin has been developed. These materials have been observed to possess high compressive strength and resistance to the aforementioned contamination, thereby leading to notable increase in plugging success rate. The compressive strength, thickening time, and resistance to formation More >

  • Open Access

    ARTICLE

    Associations between Mental Health Outcomes and Adverse Childhood Experiences and Character Strengths among University Students in Southern China

    Yulan Yu1,2, Rassamee Chotipanvithayakul3, Hujiao Kuang4, Wit Wichaidit3,*, Chonghua Wan1,2,*

    International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1343-1351, 2023, DOI:10.32604/ijmhp.2023.043446

    Abstract Adverse childhood experiences (ACEs) can negatively affect mental health, whereas character strengths seem to be positively correlated with mental health. Detailed information on the history of ACEs among university students in China and the extent which mental health is associated with ACEs and character strengths can contribute to the needed empirical evidence for relevant stakeholders. Objectives of this study are 1) to estimate the prevalence of ACEs among undergraduate students in Southern China; and 2) to assess the extent which mental health outcomes (positive growth, well-being, and depression) are associated with ACEs and character strengths… More >

  • Open Access

    ARTICLE

    Machine Learning Design of Aluminum-Lithium Alloys with High Strength

    Hongxia Wang1,2, Zhiqiang Duan2, Qingwei Guo2, Yongmei Zhang1,2,*, Yuhong Zhao2,3,4,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1393-1409, 2023, DOI:10.32604/cmc.2023.045871

    Abstract Due to the large unexplored compositional space, long development cycle, and high cost of traditional trial-anderror experiments, designing high strength aluminum-lithium alloys is a great challenge. This work establishes a performance-oriented machine learning design strategy for aluminum-lithium alloys to simplify and shorten the development cycle. The calculation results indicate that radial basis function (RBF) neural networks exhibit better predictive ability than back propagation (BP) neural networks. The RBF neural network predicted tensile and yield strengths with determination coefficients of 0.90 and 0.96, root mean square errors of 30.68 and 25.30, and mean absolute errors of More >

  • Open Access

    ARTICLE

    Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

    Daisuke Ishihara*, Naoto Takayama

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1237-1258, 2024, DOI:10.32604/cmes.2023.030211

    Abstract In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis of structure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectric coupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulations are used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely (1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weakly coupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) More >

  • Open Access

    ARTICLE

    Experimental Investigation on the Strength and Ductility Performance of SteelTimber-Steel Joints with Screw and Steel-Tube Fasteners

    Huifeng Yang, Mingwang Wu, Rixin Gu, Hang Cao, Kai Xiao, Benkai Shi*

    Journal of Renewable Materials, Vol.11, No.12, pp. 4175-4195, 2023, DOI:10.32604/jrm.2023.028507

    Abstract This article presents experimental results of steel-timber-steel (STS) joints loaded parallel to grain. Eight groups of specimens were designed, and tensile tests were performed. The fastener types and fastener numbers were considered to evaluate the tensile strengths and ductility performances of the STS joints. The screws with 6 mm diameter and the innovative steel-tubes with 18 mm diameter were adopted as connecting fasteners. The experimental results were discussed in terms of yielding and ultimate strengths, slip stiffness, and ductility factors. The ductility classification and failure mechanisms of each group of specimens were analyzed. It was… More >

  • Open Access

    ARTICLE

    Influence of Bayer Red Mud on the Operational and Mechanical Characteristics of Composite Cement Mortar

    Cheng Hu1,2, Weiheng Xiang1,3,*, Ping Chen2,3, Yi Yang4,5, Libo Zhou3, Jiufang Jiang5, Shunkai Li2,4, Yang Ming1, Qing Li3

    Journal of Renewable Materials, Vol.11, No.11, pp. 3945-3956, 2023, DOI:10.32604/jrm.2023.027544

    Abstract The aim of this study is to enhance the value and utilization of red mud generated in the Bayer process by preparing composite cement mortars. The effects of two different types of Bayer red mud with varying physical and chemical characteristics on the fluidity, mechanical strength, mineral composition, and microstructure of the composite cement mortar were systematically evaluated. The results showed that the optimal addition of red mud A was 10 wt%, while it was 20 wt% for red mud B. The mechanical properties of the composite cement mortar met the standards for P·O42.5 cement. More >

  • Open Access

    ARTICLE

    A Machine-Learning Approach for the Prediction of Fly-Ash Concrete Strength

    Shanqing Shao1, Aimin Gong1, Ran Wang1, Xiaoshuang Chen1, Jing Xu2, Fulai Wang1,*, Feipeng Liu2,3,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.12, pp. 3007-3019, 2023, DOI:10.32604/fdmp.2023.029545

    Abstract The composite exciter and the CaO to Na2SO4 dosing ratios are known to have a strong impact on the mechanical strength of fly-ash concrete. In the present study a hybrid approach relying on experiments and a machine-learning technique has been used to tackle this problem. The tests have shown that the optimal admixture of CaO and Na2SO4 alone is 8%. The best 3D mechanical strength of fly-ash concrete is achieved at 8% of the compound activator; If the 28-day mechanical strength is considered, then, the best performances are obtained at 4% of the compound activator. Moreover,… More >

  • Open Access

    ARTICLE

    A Calculation Method of Double Strength Reduction for Layered Slope Based on the Reduction of Water Content Intensity

    Feng Shen1,*, Yang Zhao1, Bingyi Li1, Kai Wu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 221-243, 2024, DOI:10.32604/cmes.2023.029159

    Abstract The calculation of the factor of safety (FOS) is an important means of slope evaluation. This paper proposed an improved double strength reduction method (DRM) to analyze the safety of layered slopes. The physical properties of different soil layers of the slopes are different, so the single coefficient strength reduction method (SRM) is not enough to reflect the actual critical state of the slopes. Considering that the water content of the soil in the natural state is the main factor for the strength of the soil, the attenuation law of shear strength of clayey soil… More >

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