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RISS 인기검색어
- 검색키워드
- 저자 : K. Ravisankar (검색결과 4 건)
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Applications of fiber optic sensors for structural health monitoring
K. Kesavan,K. Ravisankar,S. Parivallal,P. Sreeshylam 국제구조공학회 2005 Smart Structures and Systems, An International Jou Vol.1 No.4
Large and complex structures are being built now-a-days and, they are required to be functional even under extreme loading and environmental conditions. In order to meet the safety and maintenance demands, there is a need to build sensors integrated structural system, which can sense and provide necessary information about the structural response to complex loading and environment. Sophisticated tools have been developed for the design and construction of civil engineering structures. However, very little has been accomplished in the area of monitoring and rehabilitation. The employment of appropriate sensor is therefore crucial, and efforts must be directed towards non-destructive testing techniques that remain functional throughout the life of the structure. Fiber optic sensors are emerging as a superior non-destructive tool for evaluating the health of civil engineering structures. Flexibility, small in size and corrosion resistance of optical fibers allow them to be directly embedded in concrete structures. The inherent advantages of fiber optic sensors over conventional sensors include high resolution, ability to work in difficult environment, immunity from electromagnetic interference, large band width of signal, low noise and high sensitivity. This paper brings out the potential and current status of technology of fiber optic sensors for civil engineering applications. The importance of employing fiber optic sensors for health monitoring of civil engineering structures has been highlighted. Details of laboratory studies carried out on fiber optic strain sensors to assess their suitability for civil engineering applications are also covered.
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Modelling of strains in reinforced concrete flexural members using alpha-stable distribution
K. Balaji Rao,M. B. Anoop,K. Kesavan,S. R. Balasubramanian,K. Ravisankar,Nagesh R. Iyer 사단법인 한국계산역학회 2013 Computers and Concrete, An International Journal Vol.11 No.5
Large fluctuations in surface strain at the level of steel are expected in reinforced concrete flexural members at a given stage of loading due to the emergent structure (emergence of new crack patterns). This has been identified in developing deterministic constitutive models for finite element applications in Ibrahimbegovic et al. (2010). The aim of this paper is to identify a suitable probability distribution for describing the large deviations at far from equilibrium points due to emergent structures, based on phenomenological, thermodynamic and statistical considerations. Motivated by the investigations reported by Prigogine (1978) and Rubi (2008), distributions with heavy tails (namely, alpha-stable distributions) are proposed for modeling the variations in strain in reinforced concrete flexural members to account for the large fluctuations. The applicability of alpha-stable distributions at or in the neighborhood of far from equilibrium points is examined based on the results obtained from carefully planned experimental investigations, on seven reinforced concrete flexural members. It is found that alpha-stable distribution performs better than normal distribution for modeling the observed surface strains in reinforced concrete flexural members at these points.
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Applications of fiber optic sensors for structural health monitoring
Kesavan, K.,Ravisankar, K.,Parivallal, S.,Sreeshylam, P. Techno-Press 2005 Smart Structures and Systems, An International Jou Vol.1 No.4
Large and complex structures are being built now-a-days and, they are required to be functional even under extreme loading and environmental conditions. In order to meet the safety and maintenance demands, there is a need to build sensors integrated structural system, which can sense and provide necessary information about the structural response to complex loading and environment. Sophisticated tools have been developed for the design and construction of civil engineering structures. However, very little has been accomplished in the area of monitoring and rehabilitation. The employment of appropriate sensor is therefore crucial, and efforts must be directed towards non-destructive testing techniques that remain functional throughout the life of the structure. Fiber optic sensors are emerging as a superior non-destructive tool for evaluating the health of civil engineering structures. Flexibility, small in size and corrosion resistance of optical fibers allow them to be directly embedded in concrete structures. The inherent advantages of fiber optic sensors over conventional sensors include high resolution, ability to work in difficult environment, immunity from electromagnetic interference, large band width of signal, low noise and high sensitivity. This paper brings out the potential and current status of technology of fiber optic sensors for civil engineering applications. The importance of employing fiber optic sensors for health monitoring of civil engineering structures has been highlighted. Details of laboratory studies carried out on fiber optic strain sensors to assess their suitability for civil engineering applications are also covered.
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Stutee Gupta,Ashai Reddy,Sai Charan Reddy,Anusri Sakaram,K. M. Reddy,T. Ravisankar 대한공간정보학회 2023 Spatial Information Research Vol.31 No.2
As a part of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) program in India, durable assets under various work categories are created in rural areas by employing adult members belonging to the marginal communities. Taking the Prakasam district in Andhra Pradesh as an example, we analyzed the spatial clustering of the various work implemented under MGNREGA using kernel density estimation (KDE). We also analyzed the spatial clustering of villages in terms of overall assets and their statistical significance using hotspot analysis (Getis-Ord). The socio-economic factors influencing village-level clustering are analyzed using a machine learning model. Results show significant spatial variations in kernel density and village hotspots, thus, indicating the demand-driven nature of the program as influenced mainly by the marginal worker population female followed by the main other worker population. The study thus offers a methodological framework that may help improve and complement the empirical indicators currently employed in mapping the performance of the MGNREGA and similar programs.
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