Emadaldin Mohammadi Golafshani 
- (2024): A framework for low-carbon mix design of recycled aggregate concrete with supplementary cementitious materials using machine learning and optimization algorithms. In: Structures, v. 61 (März 2024).
- (2023): Prediction of rheological properties and ageing performance of recycled plastic modified bitumen using Machine learning models. In: Construction and Building Materials, v. 401 (Oktober 2023).
- (2024): Toward presenting an ensemble meta‐model for evaluation of pozzolanic mixtures incorporating industrial by‐products. In: Structural Concrete, v. 25, n. 2 (Januar 2024).
- (2024): Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network. In: Structural Concrete, v. 25, n. 2 (Januar 2024).
- (2023): Chloride diffusion modeling of concrete using tree‐based forest models. In: Structural Concrete, v. 24, n. 4 (5 Juli 2023).
- (2023): Development of seismic demand prediction models for bridges based on probability approach using symbolic regression method. In: Computers & Structures, v. 282 (Juli 2023).
- (2023): Metaheuristic‐based machine learning modeling of the compressive strength of concrete containing waste glass. In: Structural Concrete, v. 24, n. 4 (5 Juli 2023).
- (2023): Enhancing Safety Training Performance Using Extended Reality: A Hybrid Delphi–AHP Multi-Attribute Analysis in a Type-2 Fuzzy Environment. In: Buildings, v. 13, n. 3 (26 Februar 2023).
- (2022): Introduction of a novel evolutionary neural network for evaluating the compressive strength of concretes: A case of Rice Husk Ash concrete. In: Journal of Building Engineering, v. 61 (Dezember 2022).
- (2022): Efficiency of VR-Based Safety Training for Construction Equipment: Hazard Recognition in Heavy Machinery Operations. In: Buildings, v. 12, n. 12 (1 Dezember 2022).
- (2022): Estimation of the compressive strength of green concretes containing rice husk ash: a comparison of different machine learning approaches. In: European Journal of Environmental and Civil Engineering, v. 27, n. 2 (April 2022).
- (2022): Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings. In: Journal of Building Engineering, v. 46 (April 2022).
- (2021): Predicting the mechanical properties of sustainable concrete containing waste foundry sand using multi-objective ANN approach. In: Construction and Building Materials, v. 291 (Juli 2021).
- (2022): Predicting the compressive strength of self‐compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network. In: Structural Concrete, v. 23, n. 2 (April 2022).
- (2022): Seismic fragility analysis of RC box-girder bridges based on symbolic regression method. In: Structures, v. 38 (April 2022).
- (2022): Predicting the compressive strength of green concretes using Harris hawks optimization-based data-driven methods. In: Construction and Building Materials, v. 318 (Februar 2022).
- (2021): Evaluating the synergic effect of waste rubber powder and recycled concrete aggregate on mechanical properties and durability of concrete. In: Case Studies in Construction Materials, v. 15 (Dezember 2021).
- (2021): Predicting the dynamic modulus of asphalt mixture using machine learning techniques: An application of multi biogeography-based programming. In: Construction and Building Materials, v. 266 (Januar 2021).
- (2020): Scoring of Tenders in Construction Projects Using Group Method of Data Handling. In: KSCE Journal of Civil Engineering, v. 24, n. 7 (Juni 2020).
- (2020): Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm. In: Construction and Building Materials, v. 248 (Juli 2020).
- (2020): Machine learning study of the mechanical properties of concretes containing waste foundry sand. In: Construction and Building Materials, v. 243 (Mai 2020).
- (2020): Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. In: Construction and Building Materials, v. 232 (Januar 2020).
- (2020): Durability properties of concrete containing amorphous silicate tuff as a type of natural cementitious material. In: Construction and Building Materials, v. 230 (Januar 2020).
- (2014): Prediction of the bond strength of ribbed steel bars in concrete based on genetic programming. In: Computers and Concrete, v. 14, n. 3 (September 2014).
- (2012): Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic. In: Construction and Building Materials, v. 36 (November 2012).
- (2014): Bond behavior of steel and GFRP bars in self-compacting concrete. In: Construction and Building Materials, v. 61 (Juni 2014).
- (2016): Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques. In: Automation in Construction, v. 64 (April 2016).
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