A novel data-driven optimization framework for unsupervised and multivariate early-warning threshold modification in risk assessment of deep excavations

Wang, Xiong; Pan, Yue; Li, Mingguang; Chen, Jinjian

doi:10.1016/j.eswa.2023.121872

Cited by 4 publications

(1 citation statement)

References 56 publications

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“…The overwhelming amount of data, the need for dependable and high-quality data, and the moral issues related to data usage are recurring themes. Organizations have to deal with concerns about security, privacy, and managing sensitive data in an appropriate manner [23]- [25].…”

Section: Obstacles In Making Decisions Based On Datamentioning

confidence: 99%

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

Rinat,

Koli,

Sobti

et al. 2024

BIO Web Conf.

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This empirical study on Industry 5.0 offers verifiable proof of the transformational potential of data-driven decision making. The validation of data-driven choices as a key component of Industry 5.0's performance is shown by a noteworthy 46.15% increase in decision outcomes. The fact that choice criteria are in line with pertinent data sources emphasizes how important data is in forming well-informed decision-making processes. Moreover, the methodical execution and oversight of choices showcase the pragmatic significance of data-driven methodologies. This empirical evidence positions data-driven decision making as a cornerstone for improving operational efficiency, customer happiness, and market share, solidifying its essential role as the industrial environment changes. These results herald in an age when data's revolutionary potential drives industrial progress by providing a compass for companies trying to navigate the complexity of Industry 5.0.

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Section: Obstacles In Making Decisions Based On Datamentioning

confidence: 99%

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

Rinat,

Koli,

Sobti

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

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Review of recent developments in AI-based data processing and prediction for braced excavation design

Zhang,

Chang,

Goh

2024

Intelligent Transportation Infrastructure

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This paper presents a review of the recent advances in the application of Artificial intelligence (AI) techniques for data processing and prediction in the design and construction of braced excavation systems. It introduces various AI algorithms employed in addressing complex data processing and deformation prediction challenges in braced excavation. A pivotal development has been in the area of soil parameter and in-situ monitoring data processing, which facilitates more reliable site characterization for efficient design. This paper delves into wall system and deformation predictions, showcasing AI’s ability to integrate multi-source data for real-time prediction. It also addresses spatiotemporal prediction, enhancing prediction accuracy by accounting for uncertainties. The significance of groundwater is highlighted by introducing predictive models that consider groundwater drawdown. Additionally, it discusses stability prediction based on the factor of safety of braced excavation, enabling proactive safety management for the retaining wall system and the surrounding environment. Furthermore, the paper discusses the challenges and opportunities associated with the use of generative AI and large language models, providing an overview of their significance, applications, and future directions in braced excavation.

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Study on precursory recognition and integrated warning modeling to fracture in flawed sandstone under uniaxial compression

Zhang,

Meng,

Lei

et al. 2024

Fatigue Fract Eng Mat Struct

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To explore the precursory information and instability fracture of rocks, we conducted a series of uniaxial compression tests on flawed sandstone. An integrated warning modeling is developed to predict the fracture in flawed sandstone. The results show that both the peak strength and elastic modulus of flawed sandstone demonstrate an “inverted” Gaussian distribution relative to the ligament angle, reaching the minimum values at 60°. The elastic strain energy proportion shows a sharp drop‐off, whereas the proportion of dissipative energy increases in steps. The coalescence modes of flawed sandstone change from a mixed tensile‐shear failure approximately aligned with the axis to an oblique shear failure. An integrated warning model is developed by integrating a Multi‐Output Classifier (MOC) and Grid optimization (GO). The integrated warning model has an accuracy of 97.95%. Additionally, the sensitivity of the model is recorded at 99.26%, confirming its effectiveness in predicting the likelihood of fracturing.

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A novel data-driven optimization framework for unsupervised and multivariate early-warning threshold modification in risk assessment of deep excavations

Cited by 4 publications

References 56 publications

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

Data-Driven Decision Making: Real-world Effectiveness in Industry 5.0 – An Experimental Approach

Review of recent developments in AI-based data processing and prediction for braced excavation design

Study on precursory recognition and integrated warning modeling to fracture in flawed sandstone under uniaxial compression

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