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Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices打开网页
Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are emerging techniques capable of delivering elegant and affordable solutions which can surpass those obtained through traditional methods. Despite the recent and rapid advancements in developing next-gen AI-based techniques, we continue to lack a systemic understanding of how AI, ML, and DL can fundamentally be integrated into the structural engineering domain. To advocate for a smooth and expedite the adoption of AI techniques into our field , we present a state-of-the-art review that is specifically tailored to structural engineers. This review aims to serve three purposes: (1) introduce the art and science of AI, ML, and DL in terms of its commonly used algorithms and techniques with particular attention to those of high value to this domain, (2) map the current knowledge within this domain through a scientometrics analysis of more than 4000 scholarly works with a focus on those published in the last decade to identify best practices in terms of procedures, performance metrics, and dataset size etc., and (3) review past and recent efforts that applied AI derivatives into the various subfields within structural engineering. Special attention is given to the application of AI, ML, and DL in earthquake, wind, and fire engineering, as well as structural health monitoring, damage detection, and prediction of properties of structural materials as collected from over 200 sources. Finally, a discussion on trends, recommendations, best practices, and advanced topics towards the end of this review.
882022-07-24发表EngineeringComputer ScienceArchives of Computational Methods in Engineering
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Artificial Intelligence and Advanced Materials打开网页
Artificial intelligence (AI) is gaining strength, and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems, and processes can be devised and optimized thanks to machine learning (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information‐carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom, as exemplified by the power to discover unheard of materials or physical laws buried in data.
212022-09-23发表Materials ScienceComputer ScienceAdvanced Materials
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Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities打开网页
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI, particularly machine learning algorithms, range from initial image processing to high-level data understanding and knowledge discovery. AI techniques have emerged as a powerful strategy for analyzing RS data and led to remarkable breakthroughs in all RS fields. Given this period of breathtaking evolution, this work aims to provide a comprehensive review of the recent achievements of AI algorithms and applications in RS data analysis. The review includes more than 270 research papers, covering the following major aspects of AI innovation for RS: machine learning, computational intelligence, AI explicability, data mining, natural language processing (NLP), and AI security. We conclude this review by identifying promising directions for future research.
2072022-06-01发表Environmental ScienceComputer ScienceIEEE Geoscience and Remote Sensing Magazine
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人工智能（AI）领域的发展离不开一系列具有里程碑意义的论文。这些论文不仅推动了技术进步，还为后续研究奠定了基础。最具影响力的AI论文通常在方法论、应用或理论上有重大突破，对整个领域产生了深远影响。以下是几篇被广泛认为最具影响力的AI论文：
📚 最具影响力的AI论文
要点总结1：深度学习革命
- 论文："Deep Learning" by Yann LeCun, Yoshua Bengio, and Geoffrey Hinton (2015)
- 正文：这篇论文被认为是深度学习领域的奠基之作。LeCun、Bengio和Hinton三位学者在文中详细介绍了深度学习的概念、方法和应用。他们提出的卷积神经网络（CNN）和递归神经网络（RNN）等技术，极大地推动了图像识别、语音识别和自然语言处理等领域的发展。深度学习的成功应用，如AlphaGo在围棋中的胜利，进一步证明了这些方法的有效性。19
要点总结2：强化学习突破
- 论文："Playing Atari with Deep Reinforcement Learning" by Volodymyr Mnih et al. (2013)
- 正文：这篇论文介绍了深度Q网络（DQN），首次展示了如何通过强化学习训练AI系统来玩Atari游戏。DQN结合了深度学习和强化学习，能够在没有先验知识的情况下，通过试错学习复杂的策略。这一方法不仅在游戏领域取得了成功，还为机器人控制、自动驾驶等领域的研究提供了新的思路。19
要点总结3：生成对抗网络
- 论文："Generative Adversarial Nets" by Ian Goodfellow et al. (2014)
- 正文：Goodfellow等人提出的生成对抗网络（GAN）是一种通过对抗过程估计生成模型的框架。GAN由一个生成器和一个判别器组成，两者通过博弈来提高生成数据的质量。GAN在图像生成、风格迁移和数据增强等方面取得了显著成果，成为近年来最热门的研究方向之一。19
要点总结4：Transformer模型
- 论文："Attention is All You Need" by Ashish Vaswani et al. (2017)
- 正文：这篇论文介绍了Transformer模型，该模型完全基于注意力机制，摒弃了传统的循环神经网络结构。Transformer在自然语言处理任务中表现出色，特别是在机器翻译和文本生成方面。后续的BERT、GPT等模型都是基于Transformer架构，极大地推动了NLP领域的发展。19
要点总结5：AlphaGo的胜利
- 论文："Mastering the Game of Go with Deep Neural Networks and Tree Search" by David Silver et al. (2016)
- 正文：这篇论文详细描述了AlphaGo如何通过深度神经网络和蒙特卡洛树搜索相结合的方法，战胜了世界围棋冠军。AlphaGo的成功不仅展示了AI在复杂策略游戏中的潜力，还为AI在其他领域的应用提供了新的思路，如医疗诊断和金融预测。19
🔍 影响力分析
要点总结1：方法论创新
- 正文：上述论文在方法论上都有重大创新。例如，深度学习论文提出了卷积神经网络和递归神经网络，这些方法在图像和语音处理中表现出色。强化学习论文展示了如何通过试错学习复杂策略，而GAN论文则通过对抗过程提高了生成数据的质量。这些方法论的创新为后续研究提供了新的工具和思路。19
要点总结2：应用广泛
- 正文：这些论文的应用范围非常广泛。深度学习在图像识别、语音识别和自然语言处理中取得了显著成果。强化学习在游戏、机器人控制和自动驾驶中得到了应用。GAN在图像生成、风格迁移和数据增强中表现出色。Transformer模型在机器翻译、文本生成和问答系统中取得了突破。这些应用不仅展示了AI的潜力，还推动了相关领域的发展。19
要点总结3：理论贡献
- 正文：这些论文不仅在应用上取得了成功，还在理论上有重要贡献。例如，深度学习论文详细介绍了神经网络的训练方法和优化技术，为后续研究提供了理论基础。强化学习论文提出了DQN算法，为强化学习的发展奠定了基础。GAN论文通过对抗过程提出了新的生成模型框架，Transformer论文则通过注意力机制提出了新的模型架构。这些理论贡献为AI领域的发展提供了重要的支持。19
🛠️ 解决方案与建议
要点总结1：持续创新
- 正文：为了保持AI领域的持续发展，研究人员应继续探索新的方法论和应用场景。例如，可以进一步研究深度学习的优化技术，探索新的强化学习算法，开发更高效的GAN模型，以及改进Transformer架构。通过持续创新，可以推动AI技术在更多领域的应用。19
要点总结2：跨学科合作
- 正文：AI的发展需要跨学科的合作。例如，可以将AI技术应用于医疗、金融、教育等领域，通过跨学科合作解决实际问题。同时，可以借鉴其他学科的理论和方法，如心理学、经济学和生物学，来改进AI算法和模型。跨学科合作不仅可以推动AI技术的发展，还可以提高其在实际应用中的效果。19
要点总结3：伦理与监管
- 正文：随着AI技术的广泛应用，伦理和监管问题变得越来越重要。研究人员应关注AI技术的伦理影响，确保其在应用中不会对社会造成负面影响。同时，政府和监管机构应制定相应的政策和法规，规范AI技术的开发和应用，确保其安全性和可靠性。通过伦理和监管的引导，可以促进AI技术的健康发展。19
通过上述分析，可以看出这些最具影响力的AI论文在方法论、应用和理论上的重大突破，对整个领域产生了深远影响。为了保持AI领域的持续发展，研究人员应继续创新，跨学科合作，并关注伦理和监管问题。