Danial Jahed Armaghani

Civil and Structural Engineering · 2014–2021

A researcher in the Science corpus: 14 papers; trusts 4 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Civil and Structural Engineering — 59 citations into it; #5,787 of 6,160 by trust here, #2,117 by citations
• Mechanics of Materials — 24 citations into it; #6,214 of 6,291 by trust here, #3,138 by citations
• Ocean Engineering — 21 citations into it
• Safety, Risk, Reliability and Quality — 20 citations into it
• Statistics and Probability — 8 citations into it

Whom Danial Jahed Armaghani trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jian Zhou
2. Judea Pearl
3. Gérard Dreyfus
4. Leo Breiman
5. Panagiotis G. Asteris
6. Wei-Yin Loh
7. Ilker Bekir Topcu
8. Jerome H. Friedman
9. Trevor J. Hastie

Who trusts Danial Jahed Armaghani overall

1. Yingui Qiu
2. Jian Zhou
3. Panagiotis G. Asteris

Papers from Danial Jahed Armaghani's perspective

Ordered by how strongly the researchers Danial Jahed Armaghani trusts endorse each paper. Same corpus, a different order for every researcher.

1. Probabilistic reasoning in intelligent systems - networks of plausible inference
2. SPLIT SELECTION METHODS FOR CLASSIFICATION TREES
3. An Exploratory Technique for Investigating Large Quantities of Categorical Data
4. Classification and Regression Trees
5. Random Forests
6. Reinforcement Learning Trees
7. A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock (Engineering applications of artificial intelligence)
8. Artificial neural networks: fundamentals, computing, design, and application.
9. Fundamentals of fuzzy sets
10. Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68)

Their papers

• Predicting the unconfined compressive strength of granite using only two non-destructive test indexes (2021) — 81 citations
• Tensile strength prediction of rock material using non-destructive tests: A comparative intelligent study (2021) — 92 citations
• Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate (2021) — 297 citations
• Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites (2020) — 91 citations
• A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength (2020) — 429 citations
• Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques (2020) — 245 citations
• Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization (2020) — 240 citations
• Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques (2020) — 93 citations
• Mapping and holistic design of natural hydraulic lime mortars (2020) — 210 citations
• Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber (2020) — 117 citations
• Application of several optimization techniques for estimating TBM advance rate in granitic rocks (2019) — 222 citations
• Estimating the friction angle of black shale core specimens with hybrid-ANN approaches (2019) — 48 citations
• Application of fuzzy inference system for prediction of rock fragmentation induced by blasting (2015) — 101 citations
• Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization (2014) — 366 citations