KHORAMI, MAJID
Preferred name
KHORAMI, MAJID
Main Affiliation
TCEMC - Grupo de Investigación Tecnología de la Construcción, Estructuras y Materiales de Construcción
Web Site
ORCID
0000-0003-4592-0233
Scopus Author ID
57195411779
57 results
Now showing 1 - 10 of 57
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Item type:Publication, A treatment wetland park assessment model for evaluating urban ecosystem stability using analytical hierarchy process (AHP)(2019) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors(2018)Scopus© Citations 275 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of the parameters affecting the Schmidt rebound hammer reading using ANFIS method(IEEE, 2018-04)Scopus© Citations 95 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Finite Element Modelling of UHPFRC Tensile Bars(Springer International Publishing, 2021-09-05); Pedro Serna - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Portland cement structure and its major oxides and fineness(2018)Scopus© Citations 125 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of the seismic performance of special moment frames using incremental nonlinear dynamic analysis(2017); Scopus© Citations 135 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of the Influence of Sugarcane Bagasse Fibers in the Natural State on the Mechanical Properties of Concrete(Informa UK Limited, 2025-03-25)Scopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluating the Influence of Hydrophobic Nano-Silica on Cement Mixtures for Corrosion-Resistant Concrete in Green Building and Sustainable Urban Development(MDPI AG, 2023-10-26); Pablo M. Bonilla-Valladares<jats:p>The use of hydrophobic nano-silica particles in concrete for improved corrosion resistance and durability has been explored in recent years, and its potential impact on sustainable urban development and green building practices has been studied. The impact of substituting hydrophobic nano-silica particles for 2% of the cement weight in high-strength concrete mixes was investigated in this research. The study focuses on evaluating the physical-mechanical properties, including compressive strength, tensile strength, modulus of elasticity, and Poisson’s ratio. Additionally, the influence of these mixes on corrosion resistance is examined. The concrete designs feature a high strength of 42 MPa, and the hydrophilic nano-silica particles undergo functionalization processes to obtain hydrophobic properties. Contact angle measurements and water absorption tests confirm the hydrophobicity of the material. Physical, electrochemical, and electrical tests were conducted to determine the corrosion resistance contribution of the nano-silica particles when substituted at 2% of the cement weight. The research findings reveal that concrete containing nano-silica particles demonstrates improved physical-mechanical properties compared to other mixes. Incorporating nano-silica enhances concrete by accelerating hydration, increasing early-age strength, and providing hydrophobicity, resulting in improved physical-mechanical properties over other mixes. However, it was observed that the addition of hydrophobic and non-hydrophobic nano-silica tends to reduce corrosion resistance compared to concrete without these particles, despite exhibiting greater compactness. This suggests a direct influence of nano-silica on the corrosion phenomenon.</jats:p>Scopus© Citations 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Computational investigation of the comparative analysis of cylindrical barns subjected to earthquake(2018)Scopus© Citations 143 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes(Elsevier BV, 2020-07)Scopus© Citations 213