Mahmoud Alrsai

Dr. Mahmoud Alrsai

orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 November 2022 in Ocean Engineering
Verified
Journal Article
Closed access
MAMahmoud Alrsai
Mahmoud Alrsai
FAFeda'A Al-Amarat
Feda'A Al-Amarat
Hassan KarampourHassan Karampour
Hassan Karampour
HAHusam Alsanat
Husam Alsanat

13 January 2024 in Marine Structures
Verified
Journal Article
Closed access
MAMahmoud Alrsai
Mahmoud Alrsai

1 August 2024 in Ocean Engineering
Verified
Journal Article
Closed access
MAMahmoud Alrsai
Mahmoud Alrsai
AAAla’ Alsahalen
Ala’ Alsahalen
Hassan KarampourHassan Karampour
Hassan Karampour
MAMohammad Ahmad Alhawamdeh
Mohammad Ahmad Alhawamdeh
OAOmar Alajarmeh
Omar Alajarmeh

10 January 2025 in Advances in Structural Engineering
Verified
Journal Article
Closed access
MAMohammad Ahmad Alhawamdeh
Mohammad Ahmad Alhawamdeh
KAKy Eayal Awwad
Ky Eayal Awwad
MAMohammad Nour Al-Marafi
Mohammad Nour Al-Marafi
Raed Al-RbaihatRaed Al-Rbaihat
Raed Al-Rbaihat
MAMahmoud Alrsai
Mahmoud Alrsai
Despite the growing utilisation of pultruded fibre-reinforced polymers (PFRP) profiles in infrastructure applications, there is still a scarcity of design tools with a feasible computational cost. Such shortage deprives these profiles of competing with economic cost and optimised performance against other construction materials. This research investigated the numerical optimisation of cross-sectional geometry and anisotropy ratio of I-shape PFRP beams for the design against local buckling of flange under bending and web under transverse compression. A new search-adaptive micro-population genetic algorithm (SA-µP GA) tool was proposed to achieve accurate results with low computational cost. This tool was verified and compared with other GA codes from the literature. The artificial neural networks (ANNs) machine learning tool was used to maximise the use of data and generate practical and economic design charts considering the varying interactions between the design parameters. This optimisation approach represents an efficient design tool to achieve the design requirements of stability, strength, and serviceability (stiffness) limits with minimum cost and optimal structural performance. The optimisation approach was addressed using four case studies from the literature.

9 June 2019
Verified
Conference Paper
Closed access
PDF file
Hassan KarampourHassan Karampour
Hassan Karampour
MAMahmoud Alrsai
Mahmoud Alrsai
WHWayne Hall
Wayne Hall
This paper experimentally investigates the feasibility and efficiency of using Carbon Fiber Reinforced Polymer (CFRP) buckle arrestors in controlling the buckle propagation failure of subsea pipelines. Hyperbaric chamber tests are conducted on 1.6m Steel pipe with D/t = 28 and using CFRP buckle arrestors with different thickness, fiber orientation and spacing. Using an external pressure gauge and a high-pressure camera inserted inside the hyperbaric chamber, the pressure magnitude, rate and shape of collapse and its propagation in the vicinity of the arrestors are measured. The dynamics of buckle propagation and efficiency of different arrestor configurations are reported. It is observed that in the vicinity of the CFRP arrestors wrapped in the hoop direction, the well-known dog-bone buckle shape changes into a U-shape and the pressure level upsurges significantly. The optimum results were obtained with CFRP as thick as the pipeline wall-thickness and wrapped in the hoop direction of the pipeline. The results show that at similar arrestor efficiency, the CFRP arrestors can be much thinner than the existing steel slip-on arrestors. Also, the spacing between the CFRP arrestor can be larger than that of the steel slip-on arrestor.

17 September 2024 in Engineering Structures
Verified
Journal Article
Closed access
MAMahmoud Alrsai
Mahmoud Alrsai
AAAla’ Alsahalen
Ala’ Alsahalen
HKHassan Karampour
Hassan Karampour
HAHusam Alsanat
Husam Alsanat
HGHong Guan
Hong Guan
HLHong Lin
Hong Lin