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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65602
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
M.Tech Scholar, Department of Civil Engineering
Wainganga College of Engineering and Management, Nagpur, Maharashtra,India.
Assistant Professor, Department of Civil Engineering
Wainganga College of Engineering and Management, Nagpur, Maharashtra,India.
Assistant Professor, Department of Civil Engineering
Wainganga College of Engineering and Management, Nagpur, Maharashtra,India.
The rapid expansion of metro rail networks in urban areas demands innovative construction techniques to ensure speed, safety, and minimal disruption to existing infrastructure. This paper explores advanced concepts in the implementation of launching girders, focusing on their evolving role in accelerating elevated metro construction. It highlights the engineering innovations, automation strategies, and real-time monitoring technologies that are redefining girder launching methods. The study also presents a comparative analysis of traditional versus advanced launching techniques, emphasizing improvements in structural integrity, operational efficiency, and worker safety. Case studies from recent metro projects provide practical insights into deployment challenges and the effectiveness of modern solutions. This paper aims to serve as a reference for engineers and project managers seeking to optimize metro rail construction through cutting-edge girder launching technologies. Case studies from recent metro rail projects in India and abroad are analysed to demonstrate the successful implementation of these advanced concepts, with metrics including reduced construction time, enhanced safety, and cost efficiency. The challenges associated with deploying advanced launching girders such as site constraints, logistical complexity, and workforce training are also discussed along with potential mitigation strategies.
KEYWORDS
Variation of RSSI (Received Signal Strength Indicator) with distance is shown in Figure 1, Consistent with the results reported in the literature(Piyare et al. 2013). RSSI decreases with distance from the coordinator. However, the reduction in strength if plotted logarithmically will not result in a linear relationship with distance as expected. This is in conformation with (Parameswaran et al. 2009)wherein it was observed that even in ideal scenarios RSSI does not give a consistent behavior. The fluctuations at distances from 40 to 60 m in 10 bytes and 30 bytes can be correlated with reflection and multipath phenomena due to end walls and presence of electromagnetic interference from equipment such as Wi-Fi routers. This is also supported by the findings of Bae et al (2013)10 and Piyare et al (2013)29. Despite the observations that RSSI fluctuated from the theoretical expectation, all the values lie above the threshold of XBee requirements, which is -102dBm (http://www.digi.com/pdf/ds_XBeemultipointmodules.pdf [Accessed 10th January 2015]) Therefore, it can be concluded that the current wireless sensor network has enough signal strength to support data transmission in the range specified.
1.Adams, T.M., Layton, R.A. & Siahmakoun, A., 2010. Introductory MEMS Fabrication and Applications, New York: Springer-Verlag New York Inc. 2.Bae, S.C. et al., 2013. Prediction of WSN placement for bridge health monitoring based on material characteristics. Automation in Construction, 35, pp.18–27. 3.Bayrak, T., 2008. Semi Automatic Construction Progress Measurement Using a Combination of CAD Modelling , Photogrammetry and Construction Knowledge. (Thesis )Heriot-Watt University SCHOOL OF THE BUILT ENVIRONMENT. 4.Braun, A. et al., 2015. A concept for automated construction progress monitoring using BIM-based geometric constraints and photogrammetric point clouds. Journal of Information Technology in Construction, 20(November 2014), pp.68–79. 5.Brownjohn, J.M.W., 2007. Structural health monitoring of civil infrastructure. Philosophical Transactions of the Royal Society of London, Series A, Mathematical and Physical Sciences,365(1851),pp.589–622. 6.Buczak, A.L. et al., 2001. Genetic algorithm convergence study for sensor network optimization. Information Sciences, 133(3-4), pp.267–282. 7.Chen, X., Zhou, Y. & Luo, Q., 2014. A Hybrid Monkey Search Algorithm for Clustering Analysis. ,The Scientefic World Journal, 2014. 8.Cheng, A.L., Georgoulas, C. & Bock, T., 2016. Fall Detection and Intervention based on Wireless Sensor Network Technologies. Automation in Construction, p.-. 9.Cheng, M.Y. & Chen, J.C., 2002. Integrating barcode and GIS for monitoring construction progress. Automation in Construction, 11(1), pp.23–33. 10.Cheng, T. et al., 2010. Automated construction resource location tracking to support the analysis of lean principles. Challenging Lean Construction Thinking: What Do We Think and What Do We Know? - 18th Annual Conference of the International GroupmfoLean
Publication Date
6/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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