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 algorith...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.展开更多
Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major structures.Due to the complex nature of seismic events,it is challengeable to efficiently identify the ea...Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major structures.Due 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 data.These challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in general.Taking their advantages in data analysis,artificial intelligence(AI) techniques have been utilized as powerful statistical tools to tackle these issues.This typically involves processing massive detected data with severe noise to enhance the seismic performance of structures.From 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 activities.In 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 analysis.Furthermore,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.展开更多
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 accura...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.展开更多
The order components of a finite group are introduced in [12]. In [9], it is proved that the group PSL(3, q), where q is an odd prime power, is uniquely determined by its order components. In this paper, we show that ...The order components of a finite group are introduced in [12]. In [9], it is proved that the group PSL(3, q), where q is an odd prime power, is uniquely determined by its order components. In this paper, we show that the group PSL(3, q), where q=2~m, is also uniquely determined by its order components.展开更多
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 algorit...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）.展开更多
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 frequen...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 domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.展开更多
文摘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.
基金the startup fund from the Swanson School of Engineering at the University of Pittsburgh。
文摘Realistically predicting earthquake is critical for seismic risk assessment,prevention and safe design of major structures.Due 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 data.These challenges severely impact the performance of traditional seismic prediction models and obstacle the development of seismology in general.Taking their advantages in data analysis,artificial intelligence(AI) techniques have been utilized as powerful statistical tools to tackle these issues.This typically involves processing massive detected data with severe noise to enhance the seismic performance of structures.From 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 activities.In 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 analysis.Furthermore,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.
文摘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.
文摘The order components of a finite group are introduced in [12]. In [9], it is proved that the group PSL(3, q), where q is an odd prime power, is uniquely determined by its order components. In this paper, we show that the group PSL(3, q), where q=2~m, is also uniquely determined by its order components.
文摘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）.
文摘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 domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.
