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Artificial intelligence in seismology:Advent,performance and future trends

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Geoscience Frontiers 2020年第3期11卷 739-744页

作者： Pengcheng Jiao amir H.alavi Ocean College Zhejiang UniversityZhoushanZhejiang 316021China Department of Civil and Environmental Engineering University of PittsburghPittsburghPA15261USA

Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major *** to the complex nature of seismic events,it is challengeable to efficiently identify the earthquake response and extract indicative features from the continuously detected seismic *** challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in *** their advantages in data analysis,artificial intelligence(AI) techniques have been utilized as powerful statistical tools to tackle these *** typically involves processing massive detected data with severe noise to enhance the seismic performance of *** extracting meaningful sensing data to unveiling seismic events that are below the detection level,AI assists in identifying unknown features to more accurately predicting the earthquake *** this focus paper,we provide an overview of the recent AI studies in seismology and evaluate the performance of the major AI techniques including machine learning and deep learning in seismic data ***,we envision the future direction of the AI methods in earthquake engineering which will involve deep learning-enhanced seismology in an internet-of-things(IoT) platform.

关键词： Seismology Artificial intelligence Machine learning Deep learning Internet-of-Things

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

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