Panagiotis G. Asteris
Civil and Structural Engineering · 2003–2021
A researcher in the Science corpus: 11 papers; trusts 17 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Civil and Structural Engineering — 115 citations into it; #1,876 of 6,160 by trust here, #1,944 by citations
- Building and Construction — 47 citations into it; #791 of 2,909 by trust here, #1,985 by citations
- Artificial Intelligence — 15 citations into it
- Mechanics of Materials — 13 citations into it
- Management, Monitoring, Policy and Law — 11 citations into it
- Earth-Surface Processes — 9 citations into it; #129 of 722 by trust here, #332 by citations
- Safety, Risk, Reliability and Quality — 9 citations into it
- Global and Planetary Change — 8 citations into it
Whom Panagiotis G. Asteris trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Hojjat Adeli
- Geoffrey E. Hinton
- Paulo B. Lourenço
- Kurt Hornik
- Peter L. Bartlett
- Jerome H. Friedman
- Hoang Nguyen
- Simon Osindero
- E. Smyrou
- Umit Atici
Who trusts Panagiotis G. Asteris overall
- Liborio Cavaleri
- D. Cotsovos
- L. Le
- Christis Chrysostomou
- Hugo Rodrigues
- Danial Jahed Armaghani
- Fabio Di Trapani
- Hoang Nguyen
- Hai-Bang Ly
- E. Smyrou
Papers from Panagiotis G. Asteris's perspective
Ordered by how strongly the researchers Panagiotis G. Asteris trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- A Fast Learning Algorithm for Deep Belief Nets
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Deep Learning
Their papers
- Predicting the unconfined compressive strength of granite using only two non-destructive test indexes (2021) — 81 citations
- Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models (2021) — 615 citations
- A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength (2020) — 429 citations
- A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020) — 322 citations
- Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness (2020) — 97 citations
- Mapping and holistic design of natural hydraulic lime mortars (2020) — 210 citations
- Concrete compressive strength using artificial neural networks (2019) — 410 citations
- Prediction of self-compacting concrete strength using artificial neural networks (2016) — 182 citations
- Mathematical micromodeling of infilled frames: State of the art (2013) — 238 citations
- Mathematical Macromodeling of Infilled Frames: State of the Art (2011) — 322 citations
- Lateral Stiffness of Brick Masonry Infilled Plane Frames (2003) — 350 citations