Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Mohammad Khatami"'
Autor:
Seyed Mohammad Khatami, Hosein Naderpour, Alireza Mortezaei, Alireza Sharbatdar, Natalia Lasowicz, Robert Jankowski
Publikováno v:
Applied Sciences, Vol 12, Iss 10, p 4971 (2022)
The methods for preventing earthquake-induced structural pounding between two adjacent buildings include ensuring a sufficient separation distance between them or decreasing their relative displacement during seismic excitation. Some equations or eve
Externí odkaz:
https://doaj.org/article/0433f8969e82483aacb3fc9a4ea3b3d5
Publikováno v:
تاریخ ادبیات, Vol 11, Iss 1, Pp 133-155 (2018)
Karamat is a subject often discussed and categorized among supernatural wonders. Supernatural events are the type of phenomena that take place outside the normal way of the nature. The long historic background to mysticism makes us able to discuss va
Externí odkaz:
https://doaj.org/article/f5e736420599450e86fde62a141cb091
Autor:
Seyed Mohammad Khatami, Hosein Naderpour, Alireza Mortezaei, Seyed Mohammad Nazem Razavi, Natalia Lasowicz, Robert Jankowski
Publikováno v:
Applied Sciences, Vol 11, Iss 5, p 2322 (2021)
Seismic excitations may lead to collisions between adjacent civil engineering structures, causing major damage. In this paper, an effective equation for calculating the gap size index is proposed so as to provide the optimum separation distance preve
Externí odkaz:
https://doaj.org/article/8e1fe7c062e54ed7b532022ddda26acd
Autor:
Muhammad Ridho Dewanto, Yun Tonce Kusuma Priyanto, null Thomas Dwi Putra Salim, null Mohammad Khatami, null Sena Sukmananda Suprapto
Publikováno v:
Jurnal Sistim Informasi dan Teknologi.
One of the problems with solar power plants is the angle of inclination at the time of installation. Errors in determining the angle would make solar power plants do not produce power optimally. To maximize the power generated by solar power plants,
Autor:
Seyed Mohammad Khatami, Hosein Naderpour, Seyed Mohammad Nazem Razavi, Rui Carneiro Barros, Barbara Sołtysik, Robert Jankowski
Publikováno v:
Applied Sciences, Vol 10, Iss 10, p 3591 (2020)
Earthquake-induced structural pounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid col
Externí odkaz:
https://doaj.org/article/da87fe058b4f46938f0f90636e3f3b31
Autor:
Seyed Mohammad Khatami, Hosein Naderpour, Seyed Mohammad Nazem Razavi, Rui Carneiro Barros, Anna Jakubczyk-Gałczyńska, Robert Jankowski
Publikováno v:
Geosciences, Vol 10, Iss 2, p 75 (2020)
One of the possibilities to prevent building pounding between two adjacent structures is to consider appropriate in-between separation distance. Another approach might be focused on controlling the relative displacements during seismic excitations. A
Externí odkaz:
https://doaj.org/article/3297fbcc4f0344828986c2be7f639ed8
Autor:
Seyed Mohammad Khatami, Hosein Naderpour, Rui Carneiro Barros, Anna Jakubczyk-Gałczyńska, Robert Jankowski
Publikováno v:
Geosciences, Vol 10, Iss 1, p 18 (2019)
Structural pounding between adjacent, insufficiently separated buildings, or bridge segments, has been repeatedly observed during seismic excitations. Such earthquake-induced collisions may cause severe structural damage or even lead to the collapse
Externí odkaz:
https://doaj.org/article/0ac25c879c354dcb9ba78c68c9f2f1e4
Publikováno v:
Koomesh journal. 23:607-616
Autor:
Seyed Mohammad Khatami, Hosein Naderpour, Rui Carneiro Barros, Anna Jakubczyk-Gałczyńska, Robert Jankowski
Publikováno v:
Geosciences, Vol 9, Iss 8, p 347 (2019)
Structural pounding during earthquakes may cause substantial damage to colliding structures. The phenomenon is numerically studied using different models of collisions. The aim of the present paper is to propose an effective formula for the impact da
Externí odkaz:
https://doaj.org/article/df37d99a03c94cd39d9f50f6f5ae2109
Publikováno v:
Medical image analysis. 76
The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations