Pijush Samui 
- (2024): A Generalized Limit Equilibrium-Based Platform Incorporating Simplified Bishop, Janbu and Morgenstern–Price Methods for Soil Slope Stability Problems. In: Advances in Civil Engineering, v. 2024 (January 2024).
- : Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review. In: Structural Concrete.
- (2024): Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques. In: Construction and Building Materials, v. 438 (August 2024).
- : Prediction of the Splitting Tensile Strength of Manufactured Sand Based High-Performance Concrete Using Explainable Machine Learning. In: Iranian Journal of Science and Technology, Transactions of Civil Engineering.
- (2023): Reliability analysis of portal frame subjected to varied lateral loads using machine learning. In: Asian Journal of Civil Engineering, v. 25, n. 2 (October 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 (June 2023).
- (2023): Optimized ANN-based approach for estimation of shear strength of soil. In: Asian Journal of Civil Engineering, v. 24, n. 8 (June 2023).
- (2023): Probabilistic analysis of gravity retaining wall against bearing failure. In: Asian Journal of Civil Engineering, v. 24, n. 8 (June 2023).
- (2022): Design of an Energy Pile Based on CPT Data Using Soft Computing Techniques. In: Infrastructures, v. 7, n. 12 (December 2022).
- (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).
- (2021): Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. In: Cement and Concrete Research, v. 145 (July 2021).
- (2023): Prediction of secant shear modulus and damping ratio for an extremely dilative silica sand based on machine learning techniques. In: Soil Dynamics and Earthquake Engineering, v. 165 (February 2023).
- (2023): A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique. In: Structural Concrete, v. 24, n. 4 (5 July 2023).
- (2023): Implementing ensemble learning models for the prediction of shear strength of soil. In: Asian Journal of Civil Engineering, v. 24, n. 7 (April 2023).
- (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 (July 2023).
- (2022): Prediction of Probability of Liquefaction Using Soft Computing Techniques. In: Journal of The Institution of Engineers (India): Series A, v. 103, n. 4 (9 October 2022).
- (2022): A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns. In: Construction and Building Materials, v. 337 (June 2022).
- (2012): Prediction of Field Hydraulic Conductivity of Clay Liners Using an Artificial Neural Network and Support Vector Machine. In: International Journal of Geomechanics, v. 12, n. 5 (October 2012).
- (2013): Applicability of Data Mining Techniques for Predicting Electrical Resistivity of Soils Based on Thermal Resistivity. In: International Journal of Geomechanics, v. 13, n. 5 (October 2013).
- (2014): Modeling of Elastic Modulus of Jointed Rock Mass: Gaussian Process Regression Approach. In: International Journal of Geomechanics, v. 14, n. 3 (June 2014).
- (2021): Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO. In: Journal of Building Engineering, v. 35 (March 2021).
- (2016): Determination of Electrical Resistivity of Soil Based on Thermal Resistivity Using RVM and MPMR. In: Periodica Polytechnica Civil Engineering, v. 60, n. 4 ( 2016).
- (2020): Compressive strength prediction of high-performance concrete using gradient tree boosting machine. In: Construction and Building Materials, v. 264 (December 2020).
- (2020): Prediction of rapid chloride permeability of self-compacting concrete using Multivariate Adaptive Regression Spline and Minimax Probability Machine Regression. In: Journal of Building Engineering, v. 32 (November 2020).
- (2019): Determination of compressive strength using relevance vector machine and emotional neural network. In: Asian Journal of Civil Engineering, v. 20, n. 8 (August 2019).
- (2011): Model of Least Square Support Vector Machine (LSSVM) for Prediction of Fracture Parameters of Concrete. In: International Journal of Concrete Structures and Materials, v. 5, n. 1 (June 2011).
- (2014): Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine. In: KSCE Journal of Civil Engineering, v. 18, n. 6 (August 2014).
- (2013): Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach. In: Frontiers of Structural and Civil Engineering, v. 7, n. 2 (May 2013).
- (2013): Liquefaction prediction using support vector machine model based on cone penetration data. In: Frontiers of Structural and Civil Engineering, v. 7, n. 1 (February 2013).
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