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New design equations for estimation of ultimate bearing capacity of shallow foundations resting on rock masses

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Geoscience Frontiers 2016年第1期7卷 91-99页

作者： amir h.alavi Ehsan Sadrossadat Department of Civil and Environmental Engineering Michigan State University Department of Civil Engineering Mashhad BranchIslamic Azad University

Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.

关键词： Rock mass properties Ultimate bearing capacity Shallow foundation Prediction Evolutionary computation Linear genetic programming

Real-Time Detection of Cracks on Concrete Bridge Decks Using Deep Learning in the Frequency Domain

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Engineering 2021年第12期7卷 1786-1796页

作者： Qianyun Zhang Kaveh Barri Saeed K.Babanajad amir h.alavi Department of Civil and Environmental Engineering University of PittsburghPittsburghPA 15261USA Wiss JanneyElstner Associates Inc.NorthbrookIL 60062USA Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan 41354China

This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequency *** so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge *** order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing *** algorithm is then calibrated using the flattened frequency *** is used to improve the performance of the developed network for long sequence *** accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing *** implementation framework is further developed for future application of the trained model for large-scale *** proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation *** fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.

关键词： Crack detection Concrete bridge deck Deep learning Real-time

Machine learning in geosciences and remote sensing

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Geoscience Frontiers 2016年第1期7卷 3-10页

作者： David J.Lary amir h.alavi amir h.Gandomi Annette L.Walker hanson Center for Space Science University of Texas at Dallas Department of Civil and Environmental Engineering Michigan State University BEACON Center for the Study of Evolution in Action Michigan State University Aerosol and Radiation Section Naval Research Laboratory

Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

关键词： Machine learning Geosciences Remote sensing Regression Classification

Progress of machine learning in geosciences:Preface

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Geoscience Frontiers 2016年第1期7卷 1-2页

作者： amir h.alavi amir h.Gandomi David J.Lary Department of Civil and Environmental Engineering Michigan State University BEACON Center for the Study of Evolution in Action Michigan State University hanson Center for Space Science University of Texas at Dallas

In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorithms and techniques that allow computers to ＂learn＂. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system＇s deterministic model is computationally expensive or there is no deterministic model to solve the problem （Lary, 2010）.

关键词： Progress of machine learning in geosciences BPNN

Synergistic effect of adsorption and photocatalytic degradation of oilfield-produced water by electrospun photocatalytic fibers of Polystyrene/Nanorod-Graphitic carbon nitride

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Journal of Environmental Sciences 2024年第7期141卷 287-303页

作者： Parisa Azmoon Mehrdad Farhadian Alireza Pendashteh amir h.Navarchian Department of Chemical Engineering Faculty of EngineeringUniversity of IsfahanIsfahanIran Department of Chemical Engineering Faculty of EngineeringUniversity of GuilanRashtIran Department of Water and Environmental Engineering Caspian Sea Basin Research CenterUniversity of GuilanRashtIran

Graphitic carbon nitride with nanorod structure(Nr-GCN)was synthesized using melamine as a precursor without any other reagents by hydrothermal pretreatment ***,FTIR,SEM,N_(2)adsorption-desorption from BET,UV-Vis DRS spectroscopy,and photoluminescence were used to characterize the prepared ***,the photoelectrochemical behavior of nanoparticles was studied by photocurrent transient response and cyclic voltammetry ***(PS)fibrous mat was fabricated by electrospinning technique and used as a support for the stabilization of the *** performance of the synthesized nanoparticles and photocatalytic fibers(PS/Nr-GCN)was evaluated in oilfield-produced water treatment under visible light *** this process,oil contaminants were adsorbed by hydrophobic polystyrene fibers and simultaneously degraded by *** removal efficiency of chemical oxygen demand(COD)has been obtained 96.6%and 98.4%by Nr-GCN and PS/Nr-GCN,respectively,at the optimum conditions of ph4,photocatalyst dosage 0.5 g/L,COD initial concentration 550 mg/L,and illumination time 150 *** gas chromatography-mass spectroscopy analysis results showed 99.3%removal of total petroleum hydrocarbons using photocatalytic fibers of PS/*** results demonstrated that the GCN has outstanding features like controllable morphology,visible-light-driven,and showing high potential in oily wastewater ***,the synergistic effect of adsorption and photocatalytic degradation is an effective technique in oilfield-produced water treatment.

关键词： Graphitic carbon nitride(GCN) Nanorod structure Polystyrene fibrous mat Photocatalytic fibers Oilfield-produced water(OPW)

Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach

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Frontiers of Structural and Civil Engineering 2024年第3期18卷 428-443页

作者： hedye JALALI Reza YEGANEh KhAKSAR Danial MOhAMMADZADEh S. Nader KARBALLAEEZADEh amir h.GANDOMI Department of Civil Engineering Architecture and UrbanismSadjad University of TechnologyPO Box ***MashhadIran Department of Civil Engineering Faculty of EngineeringEqbal Lahoori Institute of Higher EducationPO Box ***MashhadIran Faculty of Civil Engineering Architectureand ArtScience and Research Branch of Islamic Azad UniversityPO Box ***TehranIran Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia University Research and Innovation Center(EKIK) Óbuda UniversityBudapest 1034Hungary

Fault movement during earthquakes is a geotechnical phenomenon threatening buried pipelines and with the potential to cause severe damage to critical ***,effective prediction of pipe displacement is crucial for preventive management *** study aims to develop a fast,hybrid model for predicting vertical displacement of pipe networks when they experience *** this study,the complex behavior of soil and a buried pipeline system subjected to a normal fault is analyzed by using an artificial neural network(ANN)to generate predictions the behavior of the soil when different parameters of it are *** this purpose,a finite element model is developed for a pipeline subjected to normal fault *** data bank used for training the ANN includes all the critical soil parameters(cohesion,internal friction angle,Young’s modulus,and faulting).Furthermore,a mathematical formula is presented,based on biases and weights of the ANN *** results show that the maximum error of the presented formula is 2.03%,which makes the proposed technique efficiently predict the vertical displacement of buried pipelines and hence,helps to optimize the upcoming pipeline projects.

关键词： buried pipelines normal Fault finite element method multilayer perceptron neural network formulation

Machine learning based models for predicting compressive strength of geopolymer concrete

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Frontiers of Structural and Civil Engineering 2024年第7期18卷 1028-1049页

作者： Quang-huy LE Duy-hung NGUYEN Thanh SANG-TO Samir KhATIR hoang LE-MINh amir h.GANDOMI Thanh CUONG-LE Center for Engineering Application and Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh 700000Vietnam Institute of Civil Engineering Ho Chi Minh City University of TransportHo Chi Minh 700000Vietnam Faculty of Engineering&Information Technology University of Technology SydneyUltimoNSW 2007Australia

Recently,great attention has been paid to geopolymer concrete due to its advantageous mechanical and environmentally friendly *** effort has been made in experimental studies to advance the understanding of geopolymer concrete,in which compressive strength is one of the most important *** facilitate engineering work on the material,an efficient predicting model is *** this study,three machine learning(ML)-based models,namely deep neural network(DNN),K-nearest neighbors(KNN),and support vector machines(SVM),are developed for forecasting the compressive strength of the geopolymer concrete.A total of 375 experimental samples are collected from the literature to build a database for the development of the predicting models.A careful procedure for data preprocessing is implemented,by which outliers are examined and removed from the database and input variables are standardized before feeding to the fitting *** standard K-fold cross-validation approach is applied for evaluating the performance of the models so that overfitting status is well managed,thus the generalizability of the models is *** effectiveness of the models is assessed via statistical metrics including root mean squared error(RMSE),mean absolute error(MAE),correlation coefficient(R),and the recently proposed performance index(PI).The basic mean square error(MSE)is used as the loss function to be minimized during the model fitting *** three ML-based models are successfully developed for estimating the compressive strength,for which good correlations between the predicted and the true values are obtained for DNN,KNN,and *** numerical results suggest that the DNN model generally outperforms the other two models.

关键词： geopolymer concrete compressive strength prediction machine-learning based model deep neural network K-nearest neighbor support vector machines

Davidson base as an appropriate base for the shell model in the case of 1p- and 1h- nuclei

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Communications in Theoretical Physics 2024年第10期76卷 94-100页

作者： hadi Sobhani Yan-An Luo amir Jalili hassan hassanabadi School of Physics Nankai University Department of Physics Zhejiang Sci-Tech University Institute of Theoretical Physics Faculty of Physics University of Warsaw Institute of Actuarial Science and Data Analytics UCSI University

This study endeavors to develop an improved set of basis functions for nuclear shell-model calculations. The shell model, a well-established approach to understanding nuclear structure,typically employs the harmonic oscillator potential within the mean-field approximation. Given the extensive validation and consistency of harmonic oscillator results with experimental data,we propose a modification by incorporating a centrifugal term that converges toward the Davidson potential. This potential has been extensively studied, and we aim to extract relevant experimental data for the simplest cases in the shell model, namely one-particle and one-hole nuclei. A comprehensive comparison between the Davidson-based results and the harmonic oscillator calculations is presented to demonstrate the effectiveness of the new basis functions.

关键词： beta decay gamma transition nuclear structure shell-model calculations

high-Performance Flow Classification of Big Data Using hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi hani h.Attar Ayman Amer amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster