Socio-demographic differences of disability prevalence among the population aged 60 years and over in Bangladesh

This study aims to delineate the sociodemographic differences in disability prevalence across the population aged 60 years and over in Bangladesh, and to investigate the association of factors with reporting disability in later life. A microdata sample for those aged 60 years or over from the Census of Bangladesh 2011 was used where disability was assessed with a self-reported single response question. Logistic regression models were performed separately for men and women. Results reveal that the disability prevalence rate increased sharply with age, and it was higher among older women (5.2 per cent) compared to men (4.8 per cent). Physical and vision disabilities were the two categories with the highest prevalence of reported disabilities, with a higher prevalence of physical disability among men and vision disability among women. Being older, female, currently not in marital partnership, and having a lower educational attainment, not being employed, living alone, and residing in the rural areas were significantly associated with reporting disability in later life. The higher prevalence of disability among older women, those who are illiterate, and those residing in rural areas highlights the need for policies prioritising these groups. Special attention should also be given to those who are currently not in marital partnership, particularly women who are living alone.     

Real-time Seismic Damage Detection of Concrete Shear Walls Using Artificial Neural Networks

Concrete shear walls are widely employed in buildings as a main resistance system against lateral loads. Early identification of seismic damage to concrete shear walls is vital for deciding post-earthquake occupancy in these structures. In this article, a method based on artificial neural networks for real-time identification of seismic damage to concrete shear walls was proposed. Inter-story drifts and plastic hinge rotation of concrete walls were used as the inputs and outputs of a MLP neural network. Modal Pushover Analysis was employed to prepare well-distributed data sets for training the neural network. The proposed method was applied to a five-story concrete shear wall building. The results from the network were compared with those obtained from Nonlinear Time History Analysis. It was observed that the trained neural network successfully detected damage to concrete shear walls and accurately estimated the severity of seismic-induced damage.     

Multi-actor Initiatives after Rana Plaza: Factory Managers’ Views

The Rana Plaza factory disaster in April 2013, which resulted in the death of a large number of factory workers and injured many more in Bangladesh's ready-made garment industry, highlighted the sustained failure of the government of Bangladesh to address safety in the workplace. In the wake of the tragedy two significant transnational governance initiatives emerged — the Accord on Fire and Building Safety in Bangladesh (hereafter the Accord) and the Alliance for Bangladesh Workers’ Safety (hereafter the Alliance). For the first time, different key stakeholders worked together to address fire, electrical and structural safety of factory buildings. This study analyses the perceptions of factory managers in Bangladesh regarding the Accord and Alliance agreements. The article argues that although there have been significant breakthroughs in terms of developing a culture of safety adhered to by the government and entrepreneurs, the suppliers have encountered difficulties in implementing these initiatives. The limited support from buyers has posed a major challenge for the sustainability of these two multi-stakeholder agreements.