Response estimation of reinforced concrete shear walls using artificial neural network and simulated annealing algorithm

Structural design of reinforced concrete buildings based on deep neural networks

(2021) Pablo N. Pizarro et al.   ENGINEERING STRUCTURES

Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls

(2020) Sujith Mangalathu et al.   ENGINEERING STRUCTURES

Neural network-based formula for shear capacity prediction of one-way slabs under concentrated loads

(2020) Miguel Abambres et al.   ENGINEERING STRUCTURES

Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods

(2019) H. Naderpour et al.   COMPOSITE STRUCTURES

ANN-Based Fatigue Strength of Concrete under Compression

(2019) Abambres et al.   Materials

Development of an ANN-Based Lumped Plasticity Model of RC Columns Using Historical Pseudo-Static Cyclic Test Data

(2019) Zhenliang Liu et al.   Applied Sciences-Basel

Rapid seismic damage evaluation of bridge portfolios using machine learning techniques

(2019) Sujith Mangalathu et al.   ENGINEERING STRUCTURES

Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model

(2017) X. L. Chen et al.   ENGINEERING WITH COMPUTERS

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM

(2016) K. Venkata Rao et al.   JOURNAL OF INTELLIGENT MANUFACTURING

Development of prediction models for shear strength of SFRCB using a machine learning approach

(2015) Masoud Sarveghadi et al.   NEURAL COMPUTING & APPLICATIONS

Real-time Seismic Damage Detection of Concrete Shear Walls Using Artificial Neural Networks

(2012) Mohammadreza Vafaei et al.   JOURNAL OF EARTHQUAKE ENGINEERING

Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining

(2010) Azlan Mohd Zain et al.   ENGINEERING WITH COMPUTERS

Prediction of uniaxial compressive strength of sandstones using petrography-based models

(2007) K. Zorlu et al.   ENGINEERING GEOLOGY