Prediction of Punching Shear Capacity for Fiber-Reinforced Concrete Slabs Using Neuro-Nomographs Constructed by Machine Learning
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Prediction of Punching Shear Capacity for Fiber-Reinforced Concrete Slabs Using Neuro-Nomographs Constructed by Machine Learning
Authors
Keywords
-
Journal
JOURNAL OF STRUCTURAL ENGINEERING
Volume 147, Issue 6, Pages -
Publisher
American Society of Civil Engineers (ASCE)
Online
2021-04-10
DOI
10.1061/(asce)st.1943-541x.0003041
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Nomographs for predicting allowable bearing capacity and elastic settlement of shallow foundation on granular soil
- (2019) Maher Omar et al. Arabian Journal of Geosciences
- Nomogram to help explain probabilistic seismic hazard
- (2019) John Douglas et al. JOURNAL OF SEISMOLOGY
- Development of Nomogram for Debris Flow Forecasting Based on Critical Accumulated Rainfall in South Korea
- (2019) Nam et al. Water
- Safety factor analysis of a tunnel face with an unsupported span in cohesive-frictional soils
- (2019) Xiao Zhang et al. COMPUTERS AND GEOTECHNICS
- Blast wave from a hydrogen tank rupture in a fire in the open: Hazard distance nomograms
- (2019) Sergii Kashkarov et al. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Modeling 85th Percentile Speed Using Spatially Evaluated Free-Flow Vehicles for Consistency-Based Geometric Design
- (2019) Gourab Sil et al. Journal of Transportation Engineering Part A-Systems
- Pull-off adhesion prediction of variable thick overlay to the substrate
- (2018) Łukasz Sadowski et al. AUTOMATION IN CONSTRUCTION
- Emerging artificial intelligence methods in structural engineering
- (2018) Hadi Salehi et al. ENGINEERING STRUCTURES
- A non-destructive method of the evaluation of the moisture in saline brick walls using artificial neural networks
- (2018) Adelajda Goetzke-Pala et al. Archives of Civil and Mechanical Engineering
- Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis
- (2018) Ghazi Bahroz Jumaa et al. Advances in Civil Engineering
- A novel machine learning-based algorithm to detect damage in high-rise building structures
- (2017) Mohammad Hossein Rafiei et al. STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS
- Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach
- (2015) Duy-Thang Vu et al. Structure and Infrastructure Engineering
- Effects of the fiber orientation and fiber aspect ratio on the tensile strength of Csf/Mg composites
- (2014) Wenlong Tian et al. COMPUTATIONAL MATERIALS SCIENCE
- Assessing punching shear failure in reinforced concrete flat slabs subjected to localised impact loading
- (2014) K. Micallef et al. INTERNATIONAL JOURNAL OF IMPACT ENGINEERING
- Punching shear strength of steel fibre reinforced concrete slabs
- (2012) L.F. Maya et al. ENGINEERING STRUCTURES
- Performance under Fire Situations of Concrete Members Reinforced with FRP Rods: Bond Models and Design Nomograms
- (2011) Emidio Nigro et al. JOURNAL OF COMPOSITES FOR CONSTRUCTION
- Punching Shear Capacity of Interior SFRC Slab-Column Connections
- (2011) Long Nguyen-Minh et al. JOURNAL OF STRUCTURAL ENGINEERING
- Recent trends in steel fibered high-strength concrete
- (2011) Abid A. Shah et al. MATERIALS & DESIGN
- Design Equation for Punching Shear Capacity of SFRC Slabs
- (2011) Hiroshi Higashiyama et al. International Journal of Concrete Structures and Materials
- Safety factor nomograms for homogeneous earth dams less than ten meters high
- (2009) F.J. Colomer Mendoza et al. ENGINEERING GEOLOGY
- Effects of fiber aspect ratio evaluated by elastic analysis in discontinuous composites
- (2008) Hong Gun Kim Journal of Mechanical Science and Technology
Become a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get StartedAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started