[HTML][HTML] Machine learning for pore-water pressure time-series prediction: Application of recurrent neural networks
Abstract Knowledge of pore-water pressure (PWP) variation is fundamental for slope
stability. A precise prediction of PWP is difficult due to complex physical mechanisms and in …
stability. A precise prediction of PWP is difficult due to complex physical mechanisms and in …
Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction
P Zhang, ZY Yin, YF Jin - Canadian Geotechnical Journal, 2022 - cdnsciencepub.com
This study adopts the Bayesian neural network (BNN) integrated with a strong non-linear
fitting capability and uncertainty, which has not previously been used in geotechnical …
fitting capability and uncertainty, which has not previously been used in geotechnical …
An artificial intelligence based data-driven method for forecasting unconfined compressive strength of cement stabilized soil by deep mixing technique
SF F. Mojtahedi, A Ahmadihosseini… - Geotechnical and …, 2023 - Springer
The cost constraints imposed on construction projects specially ground improvements by the
deep mixing technique (DMT) highlight the role of efficient optimization. In this study, a …
deep mixing technique (DMT) highlight the role of efficient optimization. In this study, a …
Artificial neural networks applied for solidified soils data prediction: a bibliometric and systematic review
Purpose The purpose of this article is to analyze the state-of-the art in a systematic way,
identifying the main research groups and their related topics. The types of studies found are …
identifying the main research groups and their related topics. The types of studies found are …
Assessing the vulnerability of Iran to subsidence hazard using a hierarchical FUCOM-GIS framework
In engineering practice, land subsidence is considered a threat to the infrastructure and
lifelines massively distributed globally. In this study, a combined methodology of full …
lifelines massively distributed globally. In this study, a combined methodology of full …
Bio-inspired predictive models development for strength characterization of cement deep-mixed plastic soils
FF Mojtahedi, A Ahmadihosseini, DR Eidgahee… - International Journal of …, 2024 - Springer
This paper utilizes various artificial intelligence models to predict the experimental results of
the deep-mixing technology for ground improvement and stabilization. A total of 192 …
the deep-mixing technology for ground improvement and stabilization. A total of 192 …
An automated snow mapper powered by machine learning
Snow preserves fresh water and impacts regional climate and the environment. Enabled by
modern satellite Earth observations, fast and accurate automated snow mapping is now …
modern satellite Earth observations, fast and accurate automated snow mapping is now …
ANN model development for air permeability in biochar amended unsaturated soil
Passage of municipal waste induced greenhouse gases such as carbon di oxide (CO 2) and
methane in landfill covers, majorly depends on the state of unsaturation and compaction of …
methane in landfill covers, majorly depends on the state of unsaturation and compaction of …
Slope stability of an unsaturated embankment with and without natural pore water salinity subjected to rainfall infiltration
S Hamed, K Ali… - Rock and Soil …, 2022 - rocksoilmech.researchcommons.org
Natural soils contain a certain amount of salt in the form of dissolved ions or electrically
charged atoms, originated from the long-term erosion by acidic rainwater. The dissolved salt …
charged atoms, originated from the long-term erosion by acidic rainwater. The dissolved salt …
Machine learning powered high-resolution co-seismic landslide detection
Numerous co-seismic landslides can be triggered by a strong earthquake. Fast and accurate
detection and mapping of these landslides are crucial for rapid risk assessment and …
detection and mapping of these landslides are crucial for rapid risk assessment and …