Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Autor: Ammar Alnmr, Haidar Hosamo Hosamo, Chuangxin Lyu, Richard Paul Ray, Mounzer Omran Alzawi

Publikováno v: Applied Sciences, Vol 14, Iss 11, p 4819 (2024)

This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research ut

Externí odkaz: https://doaj.org/article/a6499105d5d24cedbf93939291505ec9

A review of the Digital Twin technology for fault detection in buildings

Autor: Haidar Hosamo Hosamo, Henrik Kofoed Nielsen, Ammar Njeeb Alnmr, Paul Ragnar Svennevig, Kjeld Svidt

Publikováno v: Frontiers in Built Environment, Vol 8 (2022)

This study aims to evaluate the utilization of technology known as Digital Twin for fault detection in buildings. The strategy consisted of studying existing applications, difficulties, and possibilities that come with it. The Digital Twin technology

Externí odkaz: https://doaj.org/article/45a369693917499c9cfc41113179bbb1

A Review of the Digital Twin Technology in the AEC-FM Industry

Autor: Haidar Hosamo Hosamo, Aksa Imran, Juan Cardenas-Cartagena, Paul Ragnar Svennevig, Kjeld Svidt, Henrik Kofoed Nielsen

Publikováno v: Advances in Civil Engineering, Vol 2022 (2022)

The Architecture, Engineering, Construction, and Facility Management (AEC-FM) industry is increasingly affected by digital technologies that monitor sensor network data and control automation systems. Advances in digital technologies like Digital Twi

Externí odkaz: https://doaj.org/article/d1119d4a4f1f439a8a7c21deb7f9da87

Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Autor: Haidar Hosamo Hosamo, Henrik Kofoed Nielsen, Dimitrios Kraniotis, Paul Ragnar Svennevig, Kjeld Svidt

Publikováno v: Hosamo, H H, Nielsen, H K, Kraniotis, D, Svennevig, P R & Svidt, K 2023, ' Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings ', Energy and Buildings, vol. 281, 112732 . https://doi.org/10.1016/j.enbuild.2022.112732
Energy and Buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian n

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e7e79b46102a78e8e22cd896bf42157
https://vbn.aau.dk/da/publications/7afd35be-3f95-4f0f-8abb-8d364f2e9105

A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics

Autor: Haidar Hosamo Hosamo, Paul Ragnar Svennevig, Kjeld Svidt, Daguang Han, Henrik Kofoed Nielsen

Publikováno v: Energy and Buildings
Hosamo, H, Svennevig, P R, Svidt, K, Han, D & Nielsen, H K 2022, ' A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics ', Energy and Buildings, vol. 261, 111988 . https://doi.org/10.1016/j.enbuild.2022.111988

The building industry consumes the most energy globally, making it a priority in energy efficiency initiatives. Heating, ventilation, and air conditioning (HVAC) systems create the heart of buildings. Stable air handling unit (AHU) functioning is vit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cea6698218c357770a2ff432055ea5fb
https://hdl.handle.net/11250/3062826