Browsing by Author "Ali M"

Now showing 1 - 6 of 6
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    Artificial Neural Network (ANN) model for predicting blast-induced tunnel response in Steel Fiber Reinforced Concrete (SFRC) structures
    (Elsevier Ltd, 2025-12-01) Ali M; Chen L; Feng B; Rusho MA; Jelodar MB; Tasán Cruz DM; Samandari N
    This study presents an Artificial Neural Network (ANN)-based predictive framework for evaluating the blast-induced response of Steel Fiber Reinforced Concrete (SFRC) tunnel structures. As underground infrastructure is increasingly exposed to dynamic and extreme loading conditions, particularly from accidental or intentional explosions, accurate and efficient prediction tools are essential. In this research, a comprehensive dataset comprising 299 data points was developed, including approximately 120 experimental results from published blast and structural tests, and 179 high-fidelity numerical simulations. This combined dataset ensured both physical reliability and broad coverage of loading scenarios. The model incorporates nine critical input parameters: Peak Overpressure (MPa), Impulse (kPa·ms), Tunnel Diameter (m), Wall Thickness (m), Compressive Strength (MPa), Tensile Strength (MPa), Fiber Volume Fraction (%), Soil Stiffness (MPa/m), and Standoff Distance (m). The target output variable is the tunnel's Maximum Displacement (mm) under blast loading. A three-hidden-layer ANN architecture was optimized through rigorous hyperparameter tuning. The best-performing model, with 16 neurons in each hidden layer, achieved high predictive accuracy, with R² values of 0.983 (training), 0.956 (validation), and 0.948 (testing). Error metrics including RMSE (2.12–3.14 mm), MAE (1.92–3.52 mm), and MAPE (1.95 %–3.12 %) further confirmed the model’s robustness. Validation against experimental data from literature demonstrated excellent agreement, verifying the model's practical applicability. Additionally, sensitivity analysis identified Peak Overpressure and Standoff Distance as the most influential factors affecting displacement. The proposed ANN framework offers a computationally efficient and accurate tool for assessing SFRC tunnel performance under blast loading, supporting the design of safer and more resilient underground structures.
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    ‘Look, wait, I’ll translate’: refugee women’s experiences with interpreters in healthcare in Aotearoa New Zealand
    (CSIRO Publishing on behalf of La Trobe University, 2022-05-09) Cassim S; Kidd J; Ali M; Abdul Hamid N; Jameel D; Keenan R; Begum F; Lawrenson R
    This study aimed to explore refugee women's experiences of interpreters in healthcare in Aotearoa, New Zealand (NZ). Semi-structured interviews were conducted with nine women who arrived in NZ as refugees. Analysis involved a ‘text in context’ approach. An iterative and interpretive process was employed by engaging with participant accounts and field notes. The various meanings behind participants' experiences were unpacked in relation to the literature and the broader socio-cultural contexts in which these experiences occurred. Findings highlighted issues with professional and informal interpreters. These issues included cost, discrepancies in dialect, translation outside appointments, and privacy. Findings indicate ethical and practical implications of using interpreters in healthcare for refugee women. A step to achieving equitable healthcare for refugee women in New Zealand entails putting in place accessible and robust communicative infrastructure.
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    Project Salaam: Evaluation Report
    (Massey University, 2023-05-31) Cassim GS; Khan-Janif J; Ali M; Ali N; Shah Drew S; Slaimankhel J; Shaban N; Hodgetts D; Hopner V
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    The experiences of refugee Muslim women in the Aotearoa New Zealand healthcare system
    (Taylor and Francis Group, 2022-03) Cassim S; Ali M; Kidd J; Keenan R; Begum F; Jamil D; Abdul Hamid N; Lawrenson R
    This study explores the experiences of refugee Muslim women as they accessed and navigated the healthcare system in Aotearoa New Zealand (NZ). A case-oriented approach was used, where semi-structured interviews were carried out with nine Muslim women who arrived in NZ as refugees. Interviews were carried out in 2020, in Hamilton, NZ. Analysis involved a ‘text in context’ approach which employed an iterative and interpretive process, by engaging with participant accounts and field notes to unpack the various meanings behind the experiences of the participants in relation to the literature as well as the broader socio-cultural contexts in which these experiences occurred. The findings of this research identified various structural barriers to accessing healthcare such as cost and issues with interpreters, as well as instances of othering in the healthcare settings experienced by refugee Muslim women. In order to tackle inequity in the health system, structural and institutional barriers need to be addressed first, to prompt other levels of othering and discrimination to reduce over time.