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Majid Mohammadi

Researcher at Delft University of Technology

Publications -  95
Citations -  1382

Majid Mohammadi is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Ontology alignment & Artificial neural network. The author has an hindex of 15, co-authored 86 publications receiving 937 citations. Previous affiliations of Majid Mohammadi include VU University Amsterdam & International Institute of Minnesota.

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Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran

TL;DR: In this article, the authors investigated the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran.
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Bayesian best-worst method: A probabilistic group decision making model

TL;DR: A Bayesian BWM is introduced to find the aggregated final weights of criteria for a group of DMs at once, the BWM framework is meaningfully viewed from a probabilistic angle, and a Bayesian hierarchical model is tailored to compute the weights in the presence of a groupof DMs.
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Methods to Improve Infilled Frame Ductility

TL;DR: In this article, an experimental investigation was conducted to achieve engineered infilled frames in two stages, the first stage was aimed at discovering methods to raise infill frames' ductility, through testing six single-story single-bay infilled steel frames; three general techniques were studied, disregarding their manufacturing details.
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Strength and Stiffness of Masonry-Infilled Frames with Central Openings Based on Experimental Results

TL;DR: In this article, an extensive statistical analysis is conducted on experimental data to achieve a formula for the strength and stiffness of masonry-infilled frames having central openings, and the reliability of existing empirical relations was investigated, in which a reduction factor was suggested that shows the ratio of strength or stiffness of perforated infill to a similar solid one.
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Ensemble ranking : Aggregation of rankings produced by different multi-criteria decision-making methods

TL;DR: A new approach is proposed based on the half-quadratic (HQ) theory that determines an optimal weight for each of the MCDM ranking methods, which are used to compute the aggregated final ranking.