International Journal for Multiscale Computational Engineering最新文献

A grain-based discretized virtual internal bond (GB-DVIB) model for modeling micro-cracking of granular rock 基于晶粒的离散虚拟内结合（GB-DVIB）模型，用于模拟粒状岩石的微裂缝

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-09-01 DOI: 10.1615/intjmultcompeng.2024052740

Yuezong Yang, Yujie Wang, Zihan LIU

{"title":"A grain-based discretized virtual internal bond (GB-DVIB) model for modeling micro-cracking of granular rock","authors":"Yuezong Yang, Yujie Wang, Zihan LIU","doi":"10.1615/intjmultcompeng.2024052740","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2024052740","url":null,"abstract":"The meso-structure of rock essentially affects its macroscopic mechanical behaviors. Based on the discretized virtual internal bond (DVIB) model, a grain-based DVIB (GB-DVIB) model is developed to investigate the gain-scale micro-cracking process. A meso-structure generation method for granular rock is proposed within the framework of DVIB. By this method, mineral grains, grain-boundaries and voids can be generated conveniently. Based on the relationship between macro and micro-parameters in DVIB, the mechanical parameters of meso-structure obtained by experiments can be employed to calibrate the micro-bond parameters directly. The effect of mechanical parameters of meso-structure, grain size and porosity on the macroscopic mechanical behavior is investigated, which provides a valuable reference for the application of GB-DVIB. The intra-granular and inter-granular cracks both can be reproduced by the method. A three-point bending test and an asymmetric compressive test of granite samples are simulated. The simulated micro-cracking process and macro-failure pattern are consistent with the experimental observation. The GB-DVIB provide a convenient and effective tool for researching the gain-scale micro-cracking process of granular rock.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142201526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A smoothed natural neighbour Galerkin method for flexoelectric solids 柔电固体的平滑自然邻域 Galerkin 方法

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-08-01 DOI: 10.1615/intjmultcompeng.2024053300

Juanjuan Li, Shenjie Zhou

引用次数: 0

Bird Squirrel Optimization with Deep Recurrent Neural Network forProstate Cancer Detection 利用深度递归神经网络进行鸟类松鼠优化以检测前列腺癌

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-07-01 DOI: 10.1615/intjmultcompeng.2024050495

Goddumarri Vijay Kumar, Mohammed Ismail B, Bhaskara Reddy T, Mansour Tahernezhadi, Mansoor Alam

引用次数: 0

Machine-learning-based asymptotic homogenisation and localisation of spatially varying multiscale configurations made of materials with nonlinear stress-strain relationships 基于机器学习的非线性应力应变关系材料空间变化多尺度构型的渐近同质化和定位

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-05-01 DOI: 10.1615/intjmultcompeng.2024052116

Zhengcheng Zhou, Xiaoming Bai, yichao Zhu

{"title":"Machine-learning-based asymptotic homogenisation and localisation of spatially varying multiscale configurations made of materials with nonlinear stress-strain relationships","authors":"Zhengcheng Zhou, Xiaoming Bai, yichao Zhu","doi":"10.1615/intjmultcompeng.2024052116","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2024052116","url":null,"abstract":"This article is aimed to propose a general method in support of efficient and reliable predictions of both the global and local behaviours of spatially-varying multiscale configurations made of materials bearing general nonlinear history-independent stress-strain relationships. The framework is developed based on a complementary approach that integrates asymptotic analysis with machine learning. The use of asymptotic analysis is to identify the homogenised constitutive relationship and the implicit relationships that link the local quantities of interest, say, the site where the maximum Von Mises stress lies, with other onsite mean-field quantities. As for the implementation of the proposed asymptotic formulation, the aforementioned relationships of interest are represented by neural networks using training data generated following a guideline resulting from asymptotic analysis. With the trained neural networks, the desired local behaviours can be quickly accessed at a homogenised level without explicitly resolving the microstructural configurations. The efficiency and accuracy of the proposed scheme are further demonstrated with numerical examples, and it is shown that even for fairly complex multiscale configurations, the predicting error can be maintained at a satisfactory level. Implication from the present study to speed up classical computational homogenisation schemes is also discussed.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141061025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiscale 3D TransUNet-aided Tumor Segmentation and Multi-Cascaded Model for Lung Cancer Diagnosis System from 3D CT Images with Fused Feature Pool Formation 融合特征池形成的三维 CT 图像的多尺度三维 TransUNet 辅助肿瘤分割和肺癌诊断系统的多级联模型

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-03-01 DOI: 10.1615/intjmultcompeng.2024052181

GILBERT langat, Beiji Zou, Xiaoyan Kui, Kevin Njagi

{"title":"Multiscale 3D TransUNet-aided Tumor Segmentation and Multi-Cascaded Model for Lung Cancer Diagnosis System from 3D CT Images with Fused Feature Pool Formation","authors":"GILBERT langat, Beiji Zou, Xiaoyan Kui, Kevin Njagi","doi":"10.1615/intjmultcompeng.2024052181","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2024052181","url":null,"abstract":"A deadly disease that affects people in various countries in the world is Lung Cancer (LC). The rate at which people die due to LC is high because it cannot be detected easily at its initial stage of tumor development. The lives of many people who are affected by LC are assured if it is detected in the initial stage. The diagnosis of LC is possible with conventional Computer-Aided Diagnosis (CAD). The process of diagnosis can be improved by providing the associated evaluation outcomes to the radiologists. Since the results from the process of extraction of features and segmentation of lung nodule are crucial in determining the operation of the traditional CAD system, the results from the CAD system highly depends on these processes. The LC classification from Computed Tomography (CT) images of three dimensions (3D) using a CAD system is the key aspect of this paper. The collection of the 3D-CT images from the standard data source takes place in the first stage. The obtained images are provided as input for the segmentation stage, in which a Multi-scale 3D TransUNet (M-3D-TUNet) is adopted to get the precise segmentation of the LC images. A multi-cascaded model that incorporates Residual Network (ResNet), Visual Geometry Group (VGG)-19, and DenseNet models is utilized to obtain the deep features from the segmented images. The segmented image from the M-3D-TUNet model is given as input to this multi-cascaded network. The features are obtained and fused to form the feature pool. The feature pool features are provided to the Enhanced Long Short Term Memory with Attention Mechanism (ELSTM-AM) for classification of the LC. The ELSTM-AM classifies the images as normal or healthy","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140075283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-tuning MobileNetV3 with different weight optimization algorithms for classification of denoised blood cell images using convolutional neural network 使用不同权重优化算法微调 MobileNetV3，利用卷积神经网络对去噪血细胞图像进行分类

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-02-01 DOI: 10.1615/intjmultcompeng.2024051541

M. Mohana Dhas, N. Suresh Singh

{"title":"Fine-tuning MobileNetV3 with different weight optimization algorithms for classification of denoised blood cell images using convolutional neural network","authors":"M. Mohana Dhas, N. Suresh Singh","doi":"10.1615/intjmultcompeng.2024051541","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2024051541","url":null,"abstract":"A novel method based on convolutional neural networks (CNNs) to denoise the blood cell images (BCI) is proposed in this paper. CNN is a kind of deep learning technique that specializes in retrieving information from input images instantly and capability to reduce the need for expert knowledge when extracting and selecting features. Hyper parameters like activation functions can have a direct impact on the model's performance in CNN. Hence this paper introduced a novel Improved Rectified Linear Units (I-ReLU)-CNNs approach for denoising the BCI images. In addition, the modified-ReLU and NRMSprop are the two techniques used to fine-tune the MobileNetV3 model. Then this fine-tuned MobileNetV3 model is applied for the feature extraction to remove the unwanted features from the original images. Then the Artificial Hummingbird Algorithm (AHA) based on the Manta Ray Foraging optimization algorithm (MRFOA) is proposed for feature selection. Moreover, this AHA-MRFOA is employed to ensure the development of the overall model classification by choosing only the most essential elements. The proposed model is evaluated based on the blood cell image dataset and achieves 97.86% classification accuracy.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Peridynamics simulation and influence law analysis considering rock microscopic properties 考虑岩石微观特性的围岩动力学模拟和影响规律分析

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-01-01 DOI: 10.1615/intjmultcompeng.2024049902

Haoran Wang, Chengchao Guo, Wei Sun, Haibo Wang, Xiaodong Yang, Fuming Wang

{"title":"Peridynamics simulation and influence law analysis considering rock microscopic properties","authors":"Haoran Wang, Chengchao Guo, Wei Sun, Haibo Wang, Xiaodong Yang, Fuming Wang","doi":"10.1615/intjmultcompeng.2024049902","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2024049902","url":null,"abstract":"The microscopic properties of rocks control the macroscopic mechanical properties and fracture behavior of rocks. Existing studies on the mechanical properties of rocks have focused on treating rock materials as homogeneous or defining material properties based on Weibull random distributions, which are unable to take into account the mineralogical components and porosity characteristics of rocks. In this paper, based on the theory of bonded near-field dynamics (Peridynamics, PD), the Knuth-Durstenfeld shuffling algorithm is introduced to disrupt the mineral distribution and pore parameters, and a near-field dynamics simulation method is proposed to consider the microscopic properties of rocks. The accuracy of the proposed method is verified based on SEM tests, XRD tests and mechanical property tests of sandy mudstone and fine-grained sandstone. Further, computational analyses were carried out for the rock models under different porosities. The results indicate that porosity has a significant impact on the failure mechanism of the model.","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ASSESSING SHEAR STRENGTH OF SILICA-NASH GEOPOLYMER COMPOSITE USING MOLECULAR DYNAMIC SIMULATION 应用分子动力学模拟评价硅-纳什地聚合物复合材料的抗剪强度

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-01-01 DOI: 10.1615/intjmultcompeng.2023048631

Koochul Ji, Jongmuk Won

{"title":"ASSESSING SHEAR STRENGTH OF SILICA-NASH GEOPOLYMER COMPOSITE USING MOLECULAR DYNAMIC SIMULATION","authors":"Koochul Ji, Jongmuk Won","doi":"10.1615/intjmultcompeng.2023048631","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2023048631","url":null,"abstract":"Alkali aluminosilicate hydrate (NASH) geopolymer has been utilized as an environmentally friendly binder to replace\u0000conventional cement-based binders for ground improvement. Because shear strength is one of the critical mechanical\u0000properties in assessing the performance of geopolymer-improved soils, this study investigated the shear strength of silica-NASH geopolymer (S-G-S) composite using molecular dynamic simulation to simulate the shear behavior of\u0000geopolymer-improved soils in the molecular scale. The NASH geopolymer was first successfully constructed, which\u0000showed comparable modulus of elasticity to the observed experimental results, followed by adding silica layers to\u0000develop an S-G-S composite using geometry optimization and isobaric-isothermal ensemble simulation. The obtained\u0000interfacial shear strength of the developed S-G-S composite increased as shear velocity increased. In addition, the higher interfacial shear strength of the S-G-S composite than the shear strength of geopolymer-improved soils in literature implies the shear failure of geopolymer-improved soils is unlikely to occur at the soil-geopolymer interface. The framework shown in this study can be used as a reference model to provide molecular-scale insight into the shear behavior of geopolymer-improved soils under the variation of many influencing factors (soil mineralogy, temperature, and alkali activator content).","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138525402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient segmentation model using MRI images and deep learning Techniques for Multiple Sclerosis Classification 利用磁共振成像和深度学习技术为多发性硬化症分类建立高效的分割模型

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-01-01 DOI: 10.1615/intjmultcompeng.2023050387

GILBERT langat, Beiji Zou, Xiaoyan Kui, Kevin Njagi

{"title":"Efficient segmentation model using MRI images and deep learning Techniques for Multiple Sclerosis Classification","authors":"GILBERT langat, Beiji Zou, Xiaoyan Kui, Kevin Njagi","doi":"10.1615/intjmultcompeng.2023050387","DOIUrl":"https://doi.org/10.1615/intjmultcompeng.2023050387","url":null,"abstract":"The segmentation models employing deep learning offer successful outcomes over multiple medical image complex data resources and public data resources important for huge pathologies. During the identification of multiple sclerosis, the observation of entire tumors from the Magnetic Resonance Imaging (MRI) sequence is complex. Furthermore, it is necessary to identify the small tumors from the pictures in the prognosis phase to offer good treatment. The deep learning-assisted identification models solve the issue of the imbalance data and the false positive results are more in the conventional models. Besides, these methodologies offer a good tradeoff between the precision measure and recall measure. Thus, the latest deep learning-assisted MRI image segmentation and categorization model is developed to detect multiple sclerosis at the initial stage. Here, the MRI pictures are initially gathered from traditional online databases. The gathered images are directly given to the image segmentation process, where the Multi-scale Adaptive TransResunet++ (MSAT) is adopted to perform the lesion segmentation appropriately. The attributes present in the MSAT are optimized with the support of the developed Random Opposition of Cicada Swarm Optimization (ROCSO). Then, the segmented pictures are subjected to the categorization process, where the Hybrid and Dilated Convolution-based Adaptive Residual Attention Network (HDCARAN) is utilized to categorize the lesions from the MRI images very effectively to detect the multiple sclerosis of patients. Here, the attributes present within the HDCARAN are tuned via the same ROCSO. The implementation results are analyzed through the previously dev","PeriodicalId":50350,"journal":{"name":"International Journal for Multiscale Computational Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139497804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Biomechanical Analysis of Posterior Lumbar Interbody Fusion Constructs with Four Established Scenarios 后腰椎椎间融合器结构与四种既定方案的生物力学比较分析

IF 1.4 4区工程技术

International Journal for Multiscale Computational Engineering Pub Date : 2024-01-01 DOI: 10.1615/intjmultcompeng.2023050899

Nitesh Kumar Singh, Nishant Kumar Singh

引用次数: 0