Abstract:
Bailey bridges have an elevated position in bridge design and bridge service history with
everlasting impact on current bridge design formats. With commendable service in military
domain, they are an economical alternative for permanently bridging remote areas without
any heavy machinery. Due to its modular nature and customization options, it is also an
ideal equipment for disaster management especially for climate change affected countries
like Pakistan where floods and landslides are frequent. With an estimate of hundred MBBs
(Modified Bailey Bridges) presently in use in Pakistan, relevance of MBBs has only grown.
With global OEMs shutting down repair support to the old vintage MBBs, a need was felt
to create an indigenous variant based on Bailey design more suited to the needs of Pakistan.
This can provide parts for previous MBBs and provide new sets to meet the emerging
requirements. Therefore, a project titled Kash-1 was initiated by Research and
Development Establishment, (RDE) and their industrial partner SD Steel, Lahore to meet
these requirements. This study is project-based research that forms the backbone of this
project. This is a comparative study of static loading for MBB and Kash-1 bridge (including
design economization, design finalization and load testing) based on theoretical estimates
and FEA models (on Ansys); validated by experiments. This study concludes performance
assessment based on dead and live load deflections and discusses the comparison in detail.
It also compares the research’s findings with Span/X00 deflection criteria. This study
encompasses extensive literature survey of industrial standards and peer-reviewed
literature spanning the time from Bailey bridge design to the present. This research contains
material test reports and datasheet excerpts, pictorial records of the experimentation along
with supporting data. This research opens the possibility of use of these validated 3D
models as “Digital twins” as health monitoring tool of in-situ bridges by the help of
inspection data.