Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies

M Mishra - Journal of Cultural Heritage, 2021 - Elsevier
This paper performed a systematic review of the various machine learning (ML) techniques
applied to assess the health condition of heritage buildings. More robust predictive models …

35 Years of (AI) in geotechnical engineering: state of the art

AM Ebid - Geotechnical and Geological Engineering, 2021 - Springer
It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in
geotechnical engineering, during those years many (AI) techniques were developed based …

Influence of data splitting on performance of machine learning models in prediction of shear strength of soil

QH Nguyen, HB Ly, LS Ho, N Al-Ansari… - Mathematical …, 2021 - Wiley Online Library
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …

Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach

VQ Tran, VQ Dang, LS Ho - Construction and Building Materials, 2022 - Elsevier
To reduce the environmental impact of construction and demolition waste of concrete,
recycled concrete aggregate (RCA) has been widely utilized in concrete. The compressive …

Spatial prediction of groundwater potential map** based on convolutional neural network (CNN) and support vector regression (SVR)

M Panahi, N Sadhasivam, HR Pourghasemi… - Journal of …, 2020 - Elsevier
Freshwater shortages have become much more common globally in recent years. Water
resources that are naturally available beneath the surface are capable of reversing this …

Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete

DV Dao, HB Ly, SH Trinh, TT Le, BT Pham - Materials, 2019 - mdpi.com
Geopolymer concrete (GPC) has been used as a partial replacement of Portland cement
concrete (PCC) in various construction applications. In this paper, two artificial intelligence …

Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility

W Chen, M Panahi, P Tsangaratos, H Shahabi, I Ilia… - Catena, 2019 - Elsevier
The main objective of the present study was to produce a novel ensemble data mining
technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by …

Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate

H Xu, J Zhou, P G. Asteris, D Jahed Armaghani… - Applied sciences, 2019 - mdpi.com
Predicting the penetration rate is a complex and challenging task due to the interaction
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …

Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

Q He, H Shahabi, A Shirzadi, S Li, W Chen… - Science of the total …, 2019 - Elsevier
Landslides are major hazards for human activities often causing great damage to human
lives and infrastructure. Therefore, the main aim of the present study is to evaluate and …

A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a Monte Carlo simulation

DV Dao, H Adeli, HB Ly, LM Le, VM Le, TT Le… - Sustainability, 2020 - mdpi.com
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI)
techniques, namely Gaussian Process Regression (GPR) with five different kernels …