Predictive models for concrete properties using machine learning and deep learning approaches: A review
Concrete is one of the most widely used materials in various civil engineering applications.
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Machine learning and deep learning in smart manufacturing: The smart grid paradigm
Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …
through network sensors to the Internet, a huge amount of data is generated. Machine …
Development of deep neural network model to predict the compressive strength of rubber concrete
This paper presents an innovative development process of a Deep Neural Network model to
predict the compressive strength of rubber concrete. To this goal, a rubber concrete …
predict the compressive strength of rubber concrete. To this goal, a rubber concrete …
Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector …
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices,
and can cause social upheaval and loss of life. As a result, many scientists study the …
and can cause social upheaval and loss of life. As a result, many scientists study the …
Estimation of strength, rheological parameters, and impact of raw constituents of alkali-activated mortar using machine learning and SHapely Additive exPlanations …
One-part alkali-activated material (AAM) is a new eco-friendly developed low-carbon binder
that utilizes alkaline activators in solid form. This study deals with the experimental synthesis …
that utilizes alkaline activators in solid form. This study deals with the experimental synthesis …
Mechanical properties and prediction of fracture parameters of geopolymer/alkali-activated mortar modified with PVA fiber and nano-SiO2
P Zhang, K Wang, J Wang, J Guo, S Hu, Y Ling - Ceramics International, 2020 - Elsevier
Abstract Properties of fly ash (FA) and metakaolin (MK) based geopolymer/alkali-activated
mortar modified with polyvinyl alcohol (PVA) fiber and nano-SiO 2, including workability …
mortar modified with polyvinyl alcohol (PVA) fiber and nano-SiO 2, including workability …
Development of advanced artificial intelligence models for daily rainfall prediction
In this study, the main objective is to develop and compare several advanced Artificial
Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized …
Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized …
Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches
In this research, two different machine learning approaches are proposed for predicting the
compressive strength of fly ash based geopolymer concrete. Experimental work with a total …
compressive strength of fly ash based geopolymer concrete. Experimental work with a total …
Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate
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 …
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
[HTML][HTML] Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer
This study used three artificial intelligence-based algorithms–adaptive neuro-fuzzy inference
system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) …
system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) …