Developing an Explainable Artificial Intelligent (XAI) Model for Predicting Pile Driving Vibrations in Bangkok's Subsoil

• URL: http://arxiv.org/abs/2409.05918v1
• Date: Sun, 8 Sep 2024 10:13:35 GMT
• Title: Developing an Explainable Artificial Intelligent (XAI) Model for Predicting Pile Driving Vibrations in Bangkok's Subsoil
• Authors: Sompote Youwai, Anuwat Pamungmoon,
• Abstract summary: This study presents an explainable artificial intelligent (XAI) model for predicting pile driving vibrations in Bangkok's soft clay subsoil. A deep neural network was developed using a dataset of 1,018 real-world pile driving measurements. The model achieved a mean absolute error (MAE) of 0.276, outperforming traditional empirical methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents an explainable artificial intelligent (XAI) model for predicting pile driving vibrations in Bangkok's soft clay subsoil. A deep neural network was developed using a dataset of 1,018 real-world pile driving measurements, encompassing variations in pile dimensions, hammer characteristics, sensor locations, and vibration measurement axes. The model achieved a mean absolute error (MAE) of 0.276, outperforming traditional empirical methods and other machine learning approaches such as XGBoost and CatBoost. SHapley Additive exPlanations (SHAP) analysis was employed to interpret the model's predictions, revealing complex relationships between input features and peak particle velocity (PPV). Distance from the pile driving location emerged as the most influential factor, followed by hammer weight and pile size. Non-linear relationships and threshold effects were observed, providing new insights into vibration propagation in soft clay. A web-based application was developed to facilitate adoption by practicing engineers, bridging the gap between advanced machine learning techniques and practical engineering applications. This research contributes to the field of geotechnical engineering by offering a more accurate and nuanced approach to predicting pile driving vibrations, with implications for optimizing construction practices and mitigating environmental impacts in urban areas. The model and its source code are publicly available, promoting transparency and reproducibility in geotechnical research.

Related papers

• Robustness of Explainable Artificial Intelligence in Industrial Process Modelling [43.388607981317016]
  We evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. We show the differences between XAI methods in their ability to correctly predict the true sensitivity of the modeled industrial process.
  arXiv  Detail & Related papers  (2024-07-12T09:46:26Z)
• Explainable AI models for predicting liquefaction-induced lateral spreading [1.6221957454728797]
  Machine learning can improve lateral spreading prediction models. The "black box" nature of machine learning models can hinder their adoption in critical decision-making. This work highlights the value of explainable machine learning for reliable and informed decision-making.
  arXiv  Detail & Related papers  (2024-04-24T16:25:52Z)
• A Physics-guided Generative AI Toolkit for Geophysical Monitoring [13.986582633154226]
  Full-waveform inversion (FWI) plays a vital role in geoscience to explore the subsurface. We introduce the EdGeo toolkit, which employs a diffusion-based model guided by physics principles to generate high-fidelity velocity maps.
  arXiv  Detail & Related papers  (2024-01-06T06:09:05Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)
• Reconstruction and analysis of negatively buoyant jets with interpretable machine learning [0.0]
  negatively inclined buoyant jets are observed during the discharge of wastewater from processes such as desalination. To minimize harmful effects and assess environmental impact, a detailed numerical investigation is necessary. The application of machine learning models is proposed.
  arXiv  Detail & Related papers  (2022-11-10T11:14:45Z)
• Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading [0.0]
  This paper proposes a machine learning based surrogate model framework to predict earthquakes. The framework is validated by using it to successfully predict the peak response of one-story and three-story buildings.
  arXiv  Detail & Related papers  (2022-06-12T11:57:58Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea [58.720142291102135]
  The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focus on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.
  arXiv  Detail & Related papers  (2021-03-03T18:51:46Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.