Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Irindu Upasiri"'
Autor:
Thadshajini Suntharalingam, Perampalam Gatheeshgar, Irindu Upasiri, Keerthan Poologanathan, Brabha Nagaratnam, Marco Corradi, Dilini Nuwanthika
Publikováno v:
Case Studies in Construction Materials, Vol 15, Iss , Pp e00586- (2021)
The 3-Dimensional (3D) printing technology in the construction sector has seen an accelerating growth owing to its potential advantages. For this layer-based construction, a detailed investigation on fire performance is necessary. However, there are
Externí odkaz:
https://doaj.org/article/4b67ad8f133548729c0ae8afac4c0b25
Autor:
Thadshajini Suntharalingam, Irindu Upasiri, Brabha Nagaratnam, Keerthan Poologanathan, Perampalam Gatheeshgar, Konstantinos Daniel Tsavdaridis, Dilini Nuwanthika
Publikováno v:
Buildings, Vol 12, Iss 2, p 111 (2022)
Large-scale additive manufacturing (AM), also known as 3D concrete printing, is becoming well-recognized and, therefore, has gained intensive research attention. However, this technology requires appropriate specifications and standard guidelines. Fu
Externí odkaz:
https://doaj.org/article/4b7d8e75740f429082dde86a0c755931
Autor:
Heshachanaa Rajanayagam, Irindu Upasiri, Keerthan Poologanathan, Perampalam Gatheeshgar, Paul Sherlock, Chaminda Konthesingha, Brabha Nagaratnam, Dilini Perera
Publikováno v:
Buildings, Vol 11, Iss 12, p 621 (2021)
Lightweight Steel Frames (LSF) in building construction are becoming more popular due to their fast, clean, and flexible constructability. Typical LSF wall panels are made of cold-formed and thin-walled steel lipped channel studs with plasterboard li
Externí odkaz:
https://doaj.org/article/d9d381e93bbb4324828f34241e8cc8fb
Autor:
Thadshajini Suntharalingam, Irindu Upasiri, Perampalam Gatheeshgar, Keerthan Poologanathan, Brabha Nagaratnam, Paulo Santos, Heshachanaa Rajanayagam
Publikováno v:
Buildings, Vol 11, Iss 10, p 432 (2021)
Three-dimensional-printed concrete (3DPC), which is also termed as digital fabrication of concrete, offers potential development towards a sustainable built environment. This novel technique clearly reveals its development towards construction applic
Externí odkaz:
https://doaj.org/article/bd051f885ddc4ecb882b9a9b7dd08679
Autor:
Irindu Upasiri, Chaminda Konthesingha, Anura Nanayakkara, Keerthan Poologanathan, Gatheeshgar Perampalam, Dilini Perera
Publikováno v:
Journal of Structural Fire Engineering. 13:506-534
PurposeLight-Gauge Steel Frame (LSF) structures are popular in building construction due to their lightweight, easy erecting and constructability characteristics. However, due to steel lipped channel sections negative fire performance, cavity insulat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea3d3d0e6eefa71575e909567a48249a
https://doi.org/10.1108/jsfe-10-2021-0066
https://doi.org/10.1108/jsfe-10-2021-0066
Publikováno v:
Journal of Structural Fire Engineering.
PurposeElevated temperature material properties are essential in predicting structural member's behavior in high-temperature exposures such as fire. Even though experimental methodologies are available to determine these properties, advanced equipmen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a33c9c354bfa1bfdba1de3d5c498b101
https://doi.org/10.1108/jsfe-09-2022-0030
https://doi.org/10.1108/jsfe-09-2022-0030
Autor:
Madhushan Dissanayake, Hoang Nguyen, Keerthan Poologanathan, Gatheeshgar Perampalam, Irindu Upasiri, Heshachanaa Rajanayagam, Thadshajini Suntharalingam
This study presents the application of popular machine learning algorithms in prediction of the shear resistance of steel channel sections using experimental and numerical data. Datasets of 108 results of stainless steel lipped channel sections and 2
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e63e5fc3fcfb1547db32078972564c90
https://nrl.northumbria.ac.uk/id/eprint/48861/1/Machine_learning_for_shear_capacity_predictions.pdf
https://nrl.northumbria.ac.uk/id/eprint/48861/1/Machine_learning_for_shear_capacity_predictions.pdf
