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

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

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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

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The Colony Predation Algorithm

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Journal of Bionic Engineering 2021年第3期18卷 674-710页

作者： Jiaze Tu huiling Chen Mingjing Wang amir h.gandomi College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW 2007Australia

This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in *** utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another ***,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment *** paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation *** proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering *** results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other ***,the source code of the CPA will be publicly available after publication.

关键词： Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems

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Prediction of peak ground acceleration of Iran's tectonic regions using a hybrid soft computing technique

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Geoscience Frontiers 2016年第1期7卷 75-82页

作者： Mostafa gandomi Mohsen Soltanpour Mohammad R.Zolfaghari amir h.gandomi Department of Civil Engineering Khajeh Nasir Toosi University of Technology Department of Civil Engineering The University of Akron

A new model is derived to predict the peak ground acceleration（PGA） utilizing a hybrid method coupling artificial neural network（ANN） and simulated annealing（SA）, called SA-ANN. The proposed model relates PGA to earthquake source to site distance, earthquake magnitude, average shear-wave velocity,faulting mechanisms, and focal depth. A database of strong ground-motion recordings of 36 earthquakes,which happened in Iran’s tectonic regions, is used to establish the model. For more validity verification,the SA-ANN model is employed to predict the PGA of a part of the database beyond the training data domain. The proposed SA-ANN model is compared with the simple ANN in addition to 10 well-known models proposed in the literature. The proposed model performance is superior to the single ANN and other existing attenuation models. The SA-ANN model is highly correlated to the actual records（R=0.835 and r =0.0908） and it is subsequently converted into a tractable design equation.

关键词： Peak ground acceleration Artificial neural networks Simulated annealing Explicit formulation

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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

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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

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Novel integration of extreme learning machine and improved harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第5期14卷 1588-1608页

作者： Abidhan Bardhan Navid Kardani Abdel Kareem Alzo'ubi Bishwajit Roy Pijush Samui amir hgandomi Department of Civil Engineering National Institute of Technology PatnaPatna800005India Discipline of Civil and Infrastructure Engineering School of EngineeringRoyal Melbourne Institute of Technology(RMIT)MelbourneVIC3001Australia Department of Civil Engineering Abu Dhabi UniversityAbu DhabiUnited Arab Emirates School of Computer Science Engineering and Technology Bennett UniversityGreater NoidaIndia Faculty of Engineering and Information Technology University of Technology SydneySydneyNSW2007Australia

The study proposes an improved harris hawks optimization(IhhO) algorithm by integrating the standard harris hawks optimization(hhO) algorithm and mutation-based search mechanism for developing a high-performance machine learning solution for predicting soil compression index. hhO is a newly introduced meta-heuristic optimization algorithm(MOA) used to solve continuous search *** to the original hhO, the proposed IhhO can evade trapping in local optima, which in turn raises the search capabilities and enhances the search mechanism relying on mutation. Subsequently, a novel meta-heuristic-based soft computing technique called ELM-IhhO was established by integrating IhhO and extreme learning machine(ELM) to estimate soil compression index. A sum of 688 consolidation test data was collected for this purpose from an ongoing dedicated freight corridor railway project. To evaluate the generalization capability of the proposed ELM-IhhO model, a detailed comparison between ELM-IhhO and other well-established MOAs, such as particle swarm optimization,genetic algorithm, and biogeography-based optimization integrated with ELM, was performed. Based on the outcomes, the ELM-IhhO model exhibits superior performance over the other MOAs in predicting soil compression index.

关键词： Compression index Artificial intelligence Swarm intelligence Meta-heuristic optimization Dedicated freight corridor

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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)

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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

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Analysis of Ch_(4) and h_(2) Adsorption on heterogeneous Shale Surfaces Using aMolecular Dynamics Approach

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Fluid Dynamics & Materials Processing 2024年第1期20卷 31-44页

作者： Surajudeen Sikiru hassan Soleimani amir Rostami Mohammed Falalu hamza Lukmon Owolabi Afolabi Centre of Subsurface Imaging(CSI) Universiti Teknologi PETRONASTronohPerakMalaysia Department of Geosciences Universiti Teknologi PETRONASBandar Seri IskandarTronohPerakMalaysia Department of Pure&Industrial Chemistry Faculty of Physical SciencesBayero University KanoKanoNigeria Department of Mechanical Engineering Engineering Institute of TechnologyMelbourne Campus VictoriaMelbourneAustralia

Determining the adsorption of shale gas on complex surfaces remains a challenge in molecular simulation *** essentially stem from the need to create a realistic shale structure model in terms of mineral heterogeneityand ***,precise characterization of the competitive adsorption of hydrogen andmethane in shale generally requires the experimental determination of the related adsorptive *** thisstudy,the adsorption of adsorbates,methane(Ch_(4)),and hydrogen(h_(2))on heterogeneous shale surface modelsof Kaolinite,Orthoclase,Muscovite,Mica,C_(60),and Butane has been simulated in the frame of a moleculardynamic’s numerical *** results show that these behaviors are influenced by pressure and *** increasing the pressure from 500 to 2000 psi,the sorption effect for Ch_(4)significantly increasesbut shows a decline at a certain stage(if compared to h_(2)).The research findings also indicate that raw shalehas a higher capacity to adsorb Ch_(4)compared to ***,in shale,this difference is negligible.

关键词： Shale gas adsorption methane hydrogen molecular dynamic sorption

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