Generative adversarial network for geological prediction based on TBM operational data

Zhang, Chao; Liang, Minming; Song, Xueguan; Liu, Lixue; Wang, Hao; Li, Wensheng; Shi, Maolin

doi:10.1016/j.ymssp.2021.108035

Cited by 36 publications

(5 citation statements)

References 28 publications

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“…These profiles would then guide engineers to anticipate challenges and optimize construction methods accordingly. Zhang et al [53] study highlighted a 30% reduction in unexpected geotechnical challenges during tunnel constructions using GAN-generated soil profiles.…”

Section: Generative Adversarial Network (Gans) For Simulating Soil Pr...mentioning

confidence: 99%

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Akbar Firoozi,

Asghar Firoozi

2023

Artificial Intelligence

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Within the domain of geotechnical engineering, risk assessment is pivotal, acting as the linchpin for the safety, durability, and resilience of infrastructure projects. While traditional methodologies are robust, they frequently require extensive manual efforts and can prove laborious. With the onset of the digital era, machine learning (ML) introduces a paradigm shift in geotechnical risk assessment. This chapter delves into the confluence of ML and geotechnical engineering, spotlighting its enhanced predictive capabilities regarding soil behaviors, landslides, and structural resilience. Harnessing modern datasets and rich case studies, we offer an exhaustive examination that highlights the transformative role of ML in reshaping geotechnical risk assessment practices. Throughout our exploration of evolution, challenges, and future horizons, this chapter emphasizes the significance of ML in advancing and transforming geotechnical practices.

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Section: Generative Adversarial Network (Gans) For Simulating Soil Pr...mentioning

confidence: 99%

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Akbar Firoozi,

Asghar Firoozi

2023

Artificial Intelligence

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“…This characteristic provides an applicable way of exploiting a limited size of labeled samples to refine the networks. For example, some variants of GANs have been successfully used to deal with multi-fidelity surrogate modeling [11] and the TBM tunnel geological prediction [24], where only a small size of labeled samples are available. Such a characteristic motivates this paper as well.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

Li¹,

Zhang²,

Wang³

et al. 2022

Preprint

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Physics-informed neural networks (PINNs) provide a deep learning framework for numerically solving partial differential equations (PDEs), and have been widely used in a variety of PDE problems. However, there still remain some challenges in the application of PINNs: 1) the mechanism of PINNs is unsuitable (at least cannot be directly applied) to exploiting a small size of (usually very few) extra informative samples to refine the networks; and 2) the efficiency of training PINNs often becomes low for some complicated PDEs. In this paper, we propose the generative adversarial physics-informed neural network (GA-PINN), which integrates the generative adversarial (GA) mechanism with the structure of PINNs, to improve the performance of PINNs by exploiting only a small size of exact solutions to the PDEs. Inspired from the weighting strategy of the Adaboost method, we then introduce a point-weighting (PW) method to improve the training efficiency of PINNs, where the weight of each sample point is adaptively updated at each training iteration. The numerical experiments show that GA-PINNs outperform PINNs in many well-known PDEs and the PW method also improves the efficiency of training PINNs and GA-PINNs.

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“…The primary function of a generative network is to generate generated data that follow a specified distribution and are extremely similar to real data. The discriminant network's primary function is to accurately distinguish real data from data generated by the generator [35,36]. The learning process of a GAN can be described as a game in which the discriminator and generator constantly update and iterate their parameters to promote and improve one another.…”

Section: Network Structurementioning

confidence: 99%

Data Enhancement of Underwater High-Speed Vehicle Echo Signals Based on Improved Generative Adversarial Networks

et al. 2022

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Underwater target recognition is currently one of the hottest topics in computational intelligence research. However, underwater target recognition tasks based on deep learning techniques are difficult to conduct due to the shortage of acoustic echo signal samples, which results in poor training performance for existing deep learning models. Generative adversarial networks (GANs) have been widely used in data enhancement and image generation, providing a novel strategy for dealing with challenges in the research field mentioned above. To address the insufficiency of echo signal data for underwater high−speed vehicles, this paper proposes an underwater echo signal data enhancement method that uses an improved GAN based on convolution units for small sample sizes. First, we take pool test data as the training sample input and carry out data standardization, data interception, and copy−related processing work. Secondly, this paper proposes an improved generative adversarial network underwater (IGAN−UW) model to generate underwater echo signals. Finally, a CNN model combines the generated data with the original data to conduct classification training for underwater targets. Experimental results show that the IGAN−UW model is suitable for the generation of highly realistic original echo signals in cases with small sample sizes, providing a new approach to the active detection and recognition of underwater targets.

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Generative adversarial network for geological prediction based on TBM operational data

Cited by 36 publications

References 28 publications

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method

Data Enhancement of Underwater High-Speed Vehicle Echo Signals Based on Improved Generative Adversarial Networks

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