Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks

By Sang-Yong Lee, C. Lee. 2014

139 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/f43605a80b5241714a335baa5e8b12e5d09de301

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Hadi Salehi
2. Rigoberto Burgueño

In Civil and Structural Engineering

1. Serkan Kiranyaz
2. Moncef Gabbouj
3. Onur Avcı
4. Osama Abdeljaber
5. Daniel J. Inman
6. Hadi Salehi
7. Rigoberto Burgueño
8. Alper Erturk

Citations in the corpus

• A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “For example, in structural engineering, ML has been applied for structural system identification [57], seismic performance assessment [58], modeling of compressive [59] and tensile [60] concrete strength, shear capacity estimation of FRP-reinforced concrete beams [61], vibration control [62], as wel”
• Emerging artificial intelligence methods in structural engineering (2018, methodology): “[206] Prediction of shear strength Fiber reinforced polymer concrete ANN”