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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

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

Drag Optimization of A Planing Vessel Based on the Stability Criteria Limits

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China Ocean Engineering 2019年第3期33卷 365-372页

作者： Yasin MASUMI amir h.NIKSEREShT Department of Mechanical and Aerospace Engineering Shiraz University of Technology

One of the important phases of designing a craft is stability analysis and optimization of the drag force in cruise speed. In this research the longitudinal and lateral stability of a planing hull craft (DTMB 62 model 4667-1) is investigated with some semi-empirical formulae and the effects of some important design parameters are investigated on the limit of stability region. Also on the basis of these empirical formulations and by using a genetic algorithm the drag force is optimized in each constant cruise speed with the stability criteria limits at a constant beam or projected area. Aspect ratio, the longitudinal position of the gravity center and deadrise angle are the optimization parameters. The results show that the aspect ratio and the longitudinal position of the gravity center are two important parameters in optimizing the drag force and for this planing vessel the drag force can be reduced by 22%.

关键词： longitudinal stability transverse stability drag-optimization Savitsky formula

Insights into kinetic inhibition effects of MEG,PVP,and L-tyrosine aqueous solutions on natural gas hydrate formation

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Petroleum Science 2021年第2期18卷 495-508页

作者： amir Saberi Abdolmohammad Alamdari Ali Rasoolzadeh amir h.Mohammadi Department of Natural Gas Engineering School of Chemical and Petroleum EngineeringShiraz UniversityShiraz71345Iran Department of Process Engineering National Iranian Gas Company(NIGC)South Pars Gas Complex(SPGC)Phases 4&5Bushehr75391/311Iran Discipline of Chemical Engineering School of EngineeringUniversity of KwaZulu-NatalHoward College CampusKing George V AvenueDurban4041South Africa

It is necessary to understand all the prerequisites, which result in gas hydrate formation for safe design and control of a variety of processes in petroleum industry. Thermodynamic hydrate inhibitors (ThIs) are normally used to preclude gas hydrate formation by shifting hydrate stability region to lower temperatures and higher pressures. Sometimes, it is difficult to avoid hydrate formation and hydrates will form anyway. In this situation, kinetic hydrate inhibitors (KhIs) can be used to postpone formation of gas hydrates by retarding hydrate nucleation and growth rate. In this study, two kinetic parameters including natural gas hydrate formation induction time and the rate of gas consumption were experimentally investigated in the presence of monoethylene glycol (MEG), L-tyrosine, and polyvinylpyrrolidone (PVP) at various concentrations in aqueous solutions. Since hydrate formation is a stochastic phenomenon, the repeatability of each kinetic parameter was evaluated several times and the average values for the hydrate formation induction times and the rates of gas consumption are reported. The results indicate that from the view point of hydrate formation induction time, 2 wt% PVP and 20 wt% MEG aqueous solutions have the highest values and are the best choices. It is also interpreted from the results that from the view point of the rate of gas consumption, 20 wt% MEG aqueous solution yields the lowest value and is the best choice. Finally, it is concluded that the combination of PVP and MEG in an aqueous solution has a simultaneous synergistic impact on natural gas hydrate formation induction time and the rate of gas consumption. Furthermore, a semi-empirical model based on chemical kinetic theory is applied to evaluate the hydrate formation induction time data. A good agreement between the experimental and calculated hydrate formation induction time data is observed.

关键词： Gas hydrate Clathrate hydrate Natural gas Kinetic hydrate inhibitor(KhI) Induction time Kinetics

In vitro scolicidal effect of Satureja khuzistanica(Jamzad)essential oil

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Asian Pacific Journal of Tropical Biomedicine 2012年第8期2卷 616-620页

作者： Mohammad Moazeni Mohammad Jamal Saharkhiz Ali Akbar hoseini amir Mootabi alavi Department of Pathobiology School of Veterinary MedicineShiraz University Department of horticultural Science Collage of AgricultureShiraz University Graduate Student of Veterinary Medicine School of Veterinary MedicineShiraz University

Objective:To investigate the scolicidal effect of the Satureja khuzistanica(***) essential oil from aerial parts of this herbal ***:The essential oil was obtained by hydrodistillation *** chromatography(GC) and gas chromatography mass spectrometry(GC-MS) were employed to determine the chemical composition of the essential *** were collected aseptically from sheep livers containing hydatid *** were exposed to various concentrations of the oil(3,5 and 10 mg/mL) for 10,20,30,and 60 *** of protoscolices was confirmed by 0.1%eosin ***:A total of 19 compounds representing 97.6%of the total oil,were ***(94.9%) was found to be the major essential oil *** activity of *** essential oil at concentration of 3 mg/mL was 28.58,32.71,37.20 and 42.02%,*** essential oil at concentration of 5 mg/ mL killed 51.33,66.68,81.12,and 100%.of protoscolices after 10,20,30 and 60 min,respectively. One hundred scolicidal effect was observed with *** essenlial oil at the concentration of 10 mg/mL after 10 min(comparing with 7.19%for control group).Conclusions:The essenlial oil of *** is rich in carvacrol and may be used as a natural scolicidal agent.

关键词： hydatid cyst Scolicidal Essential oil Carvacrol Satureja khuzistanica

The anisotropic grain size effect on the mechanical response of polycrystals:The role of columnar grain morphology in additively manufactured metals

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Journal of Materials Science & Technology 2024年第14期181卷 240-256页

作者： S.amir h.Motaman Dilay Kibaroglu Institut für Eisenhüttenkunde RWTH Aachen UniversityIntzestraße 1Aachen 52072Germany

Additively manufactured(AM)metals exhibit highly complex microstructures,particularly in terms of grain morphology which typically features heterogeneous grain size distribution,irregular and anisotropic grain shapes,and the so-called columnar *** conventional morphological descriptors based on grain shape idealization are generally inadequate for representing complex and anisotropic grain mor-phology of AM *** primary aspect of microstructural grain morphology is the state of grain boundary spacing or grain size whose effect on the mechanical response is known to be *** this paper,we formally introduce the notion of axial grain size from which we derive mean axial grain size,effective grain size,and grain size anisotropy as robust morphological descriptors ca-pable of effectively representing highly complex grain *** instantiated a discrete sample of polycrystalline aggregate as a representative volume element(RVE)featuring random crystallographic orientation and misorientation ***,the instantiated RVE incorporates the typical mor-phological features of AM microstructures including distinctive grain size heterogeneity and anisotropic grain size owing to its pronounced columnar grain *** ensured that any anisotropy ob-served in the macroscopic mechanical response of the instantiated sample primarily originates from its underlying anisotropic grain *** RVE was then employed for mesoscale full-field crystal plasticity simulations corresponding to uniaxial tensile deformation along various axes via a spectral solver and a physics-based crystal plasticity constitutive model which was developed,calibrated,and validated in ear-lier *** the numerical analyses,we isolated the contribution of anisotropic grain size to the anisotropy in the mechanical response of polycrystalline aggregates,particularly those with the charac-teristic complex grain morphology of AM *** contribution c

关键词： Axial grain size Grain size anisotropy Strain hardening anisotropy Grain morphology Morphological texture Columnar grains Additive manufacturing