Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The American Review of Public Administration

Accurate assessment of undrained shear strength (USS) for soft sensitive clays is a great concern in geotechnical engineering practice. This study applies novel data-driven extreme gradient boosting (XGBoost) and random forest (RF) ensemble learning methods for capturing the relationships between the USS and various basic soil parameters. Based on the soil data sets from TC304 database, a general approach is developed to predict the USS of soft clays using the two machine learning methods above, where five feature variables including the preconsolidation stress (PS), vertical effective stress (VES), liquid limit (LL), plastic limit (PL) and natural water content (W) are adopted. To reduce the dependence on the rule of thumb and inefficient brute-force search, the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF. The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation (CV). It is shown that XGBoost-based and RF-based methods outperform these approaches. Besides, the XGBoost-based model provides feature importance ranks, which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.

Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

Roles of artificial intelligence in construction engineering and management: A critical review and future trends

Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.

https://dr.ntu.edu.sg/bitstream/10356/146887/2/1-s2.0-S1674987119302361-main.pdf

State-of-the-art review of soft computing applications in underground excavations

This paper proposes a novel hybrid approach that integrates wavelet packet transformation (WPT) and least-squares support vector machines (LSSVMs) to enhance the accuracy and reliability regarding the estimation of tunnel-induced ground settlement on a daily basis. The original time-domain signal, measured settlements over a given time period, is decomposed into a series of sequences using WPT. LSSVM models are then built to predict the target sequences within high- and low-frequency regions. The predicted sequences are reconstructed to recover the estimated tunnel-induced ground settlement over time. Two indicators, mean absolute error (MAE) and root mean square error (RMSE), are proposed to illustrate the correspondence between individual pairs of model predictions and actual observations for performance analysis. A realistic tunnel case in the Wuhan, China, metro system is utilized to demonstrate the feasibility and applicability of the proposed WPT-LSSVM approach. Comparisons between existing ...

Intelligent Approach to Estimation of Tunnel-Induced Ground Settlement Using Wavelet Packet and Support Vector Machines

A timely and reliable assessment of disaster impacts on highways is desired for executing evacuations, providing emergency services, and planning relief and recovery activities in disaster ...

Rapid Assessment of Disaster Impacts on Highways Using Social Media

Stochastic, discrete-event simulation modeling has emerged as a useful tool for facilitating decision making in construction. Owing to the rigidity inherent to distribution-based inputs, cu...

Bayesian Inference with Markov Chain Monte Carlo–Based Numerical Approach for Input Model Updating

Although the importance of cross-project knowledge transfer (CPKT) is increasingly valued from academia and industry, the research on the integrated CPKT mechanism within project-based orga...

Wenying Ji

Papers

Intelligent Approach to Estimation of Tunnel-Induced Ground Settlement Using Wavelet Packet and Support Vector Machines

Rapid Assessment of Disaster Impacts on Highways Using Social Media

Bayesian Inference with Markov Chain Monte Carlo–Based Numerical Approach for Input Model Updating

Integrated Framework of Horizontal and Vertical Cross-Project Knowledge Transfer Mechanism within Project-Based Organizations

Credible interval estimation for fraction nonconforming: Analytical and numerical solutions