Coupling Geotechnical Numerical Analysis with Machine Learning for Observational Method Projects

Mitelman, Amichai; Yang, Beverly; Urlainis, Alon; Elmo, Davide

doi:10.3390/geosciences13070196

Cited by 9 publications

(7 citation statements)

References 21 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, it is essential to leverage ongoing advancements in complementary fields to refine the calibration methodology. For instance, integrating machine-learning techniques such as artificial neural networks (ANNs) could assist in analyzing large empirical datasets and identifying key correlations, and coupling ML predictions with numerical analysis provides a means for the virtual validation of deterioration models [48,49]. Transferlearning methods may also help overcome limitations posed by small sample sizes [50].…”

Section: Calibration and Validation Of The Coefficientsmentioning

confidence: 99%

A Comprehensive Approach to Earthquake-Resilient Infrastructure: Integrating Maintenance with Seismic Fragility Curves

Urlainis,

Shohet

2023

Buildings

Self Cite

View full text Add to dashboard Cite

A severe seismic event can cause significant damage to infrastructure systems, resulting in severe direct and indirect consequences. A comprehensive risk-management approach is required for earthquake-resilient infrastructure. This study presents an innovative approach to seismic risk assessment and aims to integrate maintenance considerations with seismic fragility curves. The proposed methodology uniquely quantifies the impact of maintenance conditions on seismic risk, presenting a dynamic perspective of risk changes attributable to maintenance and deterioration. The methodology hinges on the hypothesis that the maintenance condition of the infrastructure and the level of deterioration impacts the seismic resilience of the infrastructure. The methodology synergizes the Building Performance Index (BPI) and the deterioration over time to evaluate their cumulative effect on fragility curves to estimate the infrastructure’s total risk over the lifecycle (TRLC). This proposed methodology is demonstrated through a case study of a low-voltage substation in Bik’at HaYarden, Israel. A Monte Carlo simulation was carried out for the specific conditions of the analyzed substation. A comprehensive sensitivity analysis was performed to understand better the effect of maintenance conditions over time on the TRLC. Key insights reveal a statistically significant correlation between infrastructure performance and maintenance and their consequential impact on the TRLC. Notably, declining maintenance conditions intensify seismic risk uncertainties. The research proposes to researchers, stakeholders, and decision-makers a novel comprehensive perspective on the indispensability of maintenance for seismic risk management and mitigation.

show abstract

Section: Calibration and Validation Of The Coefficientsmentioning

confidence: 99%

A Comprehensive Approach to Earthquake-Resilient Infrastructure: Integrating Maintenance with Seismic Fragility Curves

Urlainis,

Shohet

2023

Buildings

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nonetheless, advanced sensors, remote sensing technologies, and geotechnical investigations have enabled the collection of vast datasets. It's crucial to preprocess this data, removing outliers, handling missing values, and ensuring that the dataset is representative of the diverse conditions a project might encounter [56].…”

Section: Data Collection and Preprocessing In Geotechnical Engineeringmentioning

confidence: 99%

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

Akbar Firoozi,

Asghar Firoozi

2023

Artificial Intelligence

View full text Add to dashboard Cite

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.

show abstract

“…For deep excavations, lateral supports, such as struts or ground anchors, are installed according to a staged excavation plan. The increase in urbanized areas is pressuring the geotechnical community, which is currently challenged by a demand to better predict wall behaviour and displacement [1]. In turn, advancing geotechnical analysis methods is crucial for obtaining safe and economic structural design [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Embedded Wall Displacements Using Small-Strain Hardening Soil Model

Eilat,

Mitelman,

McQuillan

et al. 2024

Geotechnics

Self Cite

View full text Add to dashboard Cite

Traditional analysis of embedded earth-retaining walls relies on simplistic lateral earth pressure theory methods, which do not allow for direct computation of wall displacements. Contemporary numerical models rely on the Mohr–Coulomb model, which generally falls short of accurate wall displacement prediction. The advanced constitutive small-strain hardening soil model (SS-HSM), effectively captures complex nonlinear soil behavior. However, its application is currently limited, as SS-HSM requires multiple input parameters, rendering numerical modeling a challenging and time-consuming task. This study presents an extensive numerical investigation, where wall displacements from numerical models are compared to empirical findings from a large and reliable database. A novel automated computational scheme is created for model generation and advanced data analysis is undertaken for this objective. The main findings indicate that the SS-HSM can provide realistic predictions of wall displacements. Ultimately, a range of input parameters for the utilization of SS-HSM in earth-retaining wall analysis is established, providing a good starting point for engineers and researchers seeking to model more complex scenarios of embedded walls with the SS-HSM.

show abstract

Coupling Geotechnical Numerical Analysis with Machine Learning for Observational Method Projects

Cited by 9 publications

References 21 publications

A Comprehensive Approach to Earthquake-Resilient Infrastructure: Integrating Maintenance with Seismic Fragility Curves

A Comprehensive Approach to Earthquake-Resilient Infrastructure: Integrating Maintenance with Seismic Fragility Curves

Application of Machine Learning in Geotechnical Engineering for Risk Assessment

A Comparative Study of Embedded Wall Displacements Using Small-Strain Hardening Soil Model

Contact Info

Product

Resources

About