Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests

Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study

(2021) Manish Kumar et al.   Processes

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

(2021) Panagiotis G. Asteris et al.   CEMENT AND CONCRETE RESEARCH

Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions

(2021) Abidhan Bardhan et al.   ENGINEERING GEOLOGY

Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power

(2021) Mosbeh R. Kaloop et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models

(2021) SUFYAN GHANI et al.   SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES

ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions

(2021) Abidhan Bardhan et al.   APPLIED SOFT COMPUTING

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

(2020) Jin Duan et al.   ENGINEERING WITH COMPUTERS

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

(2020) Danial Jahed Armaghani et al.   Sustainability

Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO

(2020) Navid Kardani et al.   Journal of Building Engineering

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

(2019) Panagiotis G. Asteris et al.   Applied Sciences-Basel

Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

(2019) Hui Chen et al.   Applied Sciences-Basel

A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets

(2019) Hooman Harandizadeh et al.   ENGINEERING WITH COMPUTERS

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

(2019) Hai Xu et al.   Applied Sciences-Basel

Concrete compressive strength using artificial neural networks

(2019) Panagiotis G. Asteris et al.   NEURAL COMPUTING & APPLICATIONS

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

(2019) Lei Huang et al.   Applied Sciences-Basel

Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition

(2017) Danial Jahed Armaghani et al.   TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY

Advancing concrete strength prediction using non-destructive testing: Development and verification of a generalizable model

(2016) Kamran Amini et al.   CONSTRUCTION AND BUILDING MATERIALS

A new model based on gene expression programming to estimate air flow in a single rock joint

(2016) Manoj Khandelwal et al.   Environmental Earth Sciences

Neuro-fuzzy technique to predict air-overpressure induced by blasting

(2015) Danial Jahed Armaghani et al.   Arabian Journal of Geosciences

Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles

(2015) Ehsan Momeni et al.   Earth Sciences Research Journal

Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network

(2011) U. Atici   EXPERT SYSTEMS WITH APPLICATIONS

Artificial Neural Network Approach to Predict Compressive Strength of Concrete through Ultrasonic Pulse Velocity

(2010) M. Bilgehan et al.   RESEARCH IN NONDESTRUCTIVE EVALUATION

Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

(2008) Gregor Trtnik et al.   ULTRASONICS