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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo amir h.gandomi Department of Computer Science and Engineering C.V.Raman Global University Department of Computer Science and Engineering SRM University Department of Computing Science Ume? University Department of Computer Science and Engineering Faculty of Engineering and Information Technology University of Technology Sydney University Research and Innovation Center (EKIK) óbuda University

The development of the Internet of Things(IoT) technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA) is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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

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

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

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

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

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

Energy-based numerical models for assessment of soil liquefaction

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Geoscience Frontiers 2012年第4期3卷 541-555页

作者： amir hossein Alavi amir hossein gandomi School of Civil Engineering lran University of Science and TechnologyTehranIran College of Civil Engineering Tafresh UniversityTafreshlran

This study presents promising variants of genetic programming （GP）, namely linear genetic programming （LGP） and multi expression programming （MEP） to evaluate the liquefaction resistance of san- dy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction （capacity energy） and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.

关键词： Soil liquefaction Capacity energy Linear genetic programming Multi expression programming Sand Formulation

Imperialistic Competitive Algorithm:A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes

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Geoscience Frontiers 2016年第1期7卷 83-89页

作者： Ali Reza Kashani amir hossein gandomi Mehdi Mousavi Department of Civil Engineering Arak University Department of Civil Engineering The University of Akron

In this study, Imperialistic Competitive Algorithm（ICA） is utilized for locating the critical failure surface and computing the factor of safety（FOS） in a slope stability analysis based on the limit equilibrium approach. The factor of safety relating to each trial slip surface is calculated using a simplified algorithm of the Morgenstern-Price method, which satisfies both the force and the moment equilibriums. General slip surface is considered non-circular in this study that is constituted by linking random straight *** explore the performance of the proposed algorithm, four benchmark test problems are analyzed. The results demonstrate that the present techniques can provide reliable, accurate and efficient solutions for locating the critical failure surface and relating FOS. Moreover, in contrast with previous studies the present algorithm could reach the lower value of FOS and reached more exact solutions.

关键词： Meta-heuristic algorithms Morgen-stern and price method Non-circular slip surface Imperialistic competitive algorithm