Pijush Samui 
- (2024): Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites. In: Infrastructures, v. 9, n. 10 (8 Oktober 2024).
- (2024): Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns. In: Frontiers of Structural and Civil Engineering, v. 18, n. 8 (Juli 2024).
- (2024): Second-order reliability analysis of an energy pile with CPT data. In: Journal of Building Engineering, v. 95 (Oktober 2024).
- (2024): Soft computing-based reliability analysis of simply supported beam: a comparative study of hybrid ANN models. In: Asian Journal of Civil Engineering, v. 25, n. 4 (Februar 2024).
- (2023): Reliability analysis of portal frame subjected to varied lateral loads using machine learning. In: Asian Journal of Civil Engineering, v. 25, n. 2 (Oktober 2023).
- (2023): Soft computing-based prediction models for compressive strength of concrete. In: Case Studies in Construction Materials, v. 19 (Dezember 2023).
- (2023): Reliability analysis of frame structures under top-floor lateral load using artificial intelligence. In: Asian Journal of Civil Engineering, v. 24, n. 8 (Juni 2023).
- (2023): Soft-Computing Techniques for Predicting Seismic Bearing Capacity of Strip Footings in Slopes. In: Buildings, v. 13, n. 6 (23 Mai 2023).
- (2022): Reliability Analysis of Gravity Retaining Wall Using Hybrid ANFIS. In: Infrastructures, v. 7, n. 9 (September 2022).
- (2021): Rock Strain Prediction Using Deep Neural Network and Hybrid Models of ANFIS and Meta-Heuristic Optimization Algorithms. In: Infrastructures, v. 6, n. 9 (September 2021).
- (2023): Reliability analysis of reinforced soil slope stability using GA-ANFIS, RFC, and GMDH soft computing techniques. In: Case Studies in Construction Materials, v. 18 (Juli 2023).
- (2022): Stress intensity factor prediction on offshore pipelines using surrogate modeling techniques. In: Case Studies in Construction Materials, v. 16 (Juni 2022).
- (2022): Soft computing approaches towards tensile strength estimation of GFRP rebars subjected to alkaline-concrete environment. In: Case Studies in Construction Materials, v. 16 (Juni 2022).
- (2021): Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests. In: Construction and Building Materials, v. 303 (Oktober 2021).
- (2010): Site Characterization Model Using Artificial Neural Network and Kriging. In: International Journal of Geomechanics, v. 10, n. 5 (Oktober 2010).
- (2013): Use of the Relevance Vector Machine for Prediction of an Overconsolidation Ratio. In: International Journal of Geomechanics, v. 13, n. 1 (Februar 2013).
- (2012): Utilization of support vector machine for prediction of fracture parameters of concrete. In: Computers and Concrete, v. 9, n. 3 (März 2012).
- (2007): Seismic liquefaction potential assessment by using Relevance Vector Machine. In: Earthquake Engineering and Engineering Vibration, v. 6, n. 4 (Dezember 2007).
- (2018): Soft Computing Applied to Rotation Capacity of Wide Flange Beams. In: Iranian Journal of Science and Technology, Transactions of Civil Engineering, v. 42, n. 3 ( 2018).
- (2013): SPT-based liquefaction potential assessment by relevance vector machine approach. In: European Journal of Environmental and Civil Engineering, v. 17, n. 4 (April 2013).
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