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    ๊ณต์šฉ์ค‘ ์ผ€์ด๋ธ”๊ต๋Ÿ‰์˜ ๊ณ„์ธก๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ๊ฐ์‡ ๋น„ ์ถ”์ •๊ธฐ๋ฒ• ๊ฐœ์„ 

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2017. 8. ๊น€ํ˜ธ๊ฒฝ.์‘๋‹ต๊ธฐ๋ฐ˜๋ชจ๋“œํ•ด์„(Operational Modal Analysis, OMA) ๊ธฐ๋ฐ˜ ์ถ”์ • ๊ฐ์‡ ๋น„์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ž๋™ํ™”๋œ ๋ณ€์ˆ˜์„ ์ • ๋ฐ ์‹ ํ˜ธ ์ •์ƒํ™” ๊ณผ์ •์— ๊ธฐ๋ฐ˜ํ•œ ๊ฐœ์„ ๋œ Natural Excitation Techniqueโ€”Eigensystem Realization (NExT-ERA) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€์œผ๋ฉฐ, ๋‹ค์ค‘์งˆ๋Ÿ‰๋™์กฐ๊ฐ์‡ ๊ธฐ(Multiple Tuned Mass Damper, MTMD) ์„ค์น˜ ์ „ํ›„์˜ ์‚ฌ์žฅ๊ต ๊ณ„์ธก ๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜์—ฌ ๊ทธ ์ •๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์ƒ๊ด€๋ถ„์„์„ ํ†ตํ•ด ํ™˜๊ฒฝ์  ์š”์ธ์˜ ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ณ  ๊ฐ์‡ ๋น„ ๋ณ€ํ™”์— ๊ฐ€์žฅ ์ฃผ์š”ํ•œ ์›์ธ์ด ๋˜๋Š” ํ™˜๊ฒฝ์  ์š”์ธ์„ ์„ ์ •, ํšŒ๊ท€๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ• ์ ์šฉ ์ „ํ›„์— ๋”ฐ๋ผ ๊ฐ์‡ ๋น„์˜ ๋ถˆํ™•์‹ค์„ฑ์ด ์–ผ๋งˆ๋‚˜ ์ค„์–ด๋“œ๋Š”์ง€๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฒฝํ—˜์  ๋ณ€์ˆ˜์„ ํƒ์— ์ค€ํ•˜๋Š” ๊ฐ์‡ ๋น„ ์ถ”์ •์˜ ์ •ํ™•๋„ ๋ฐ ์ •๋ฐ€๋„์˜ ํ™•๋ณด๋ฅผ ๋ชฉํ‘œ๋กœ ์ž๋™ํ™”๋œ ๋ณ€์ˆ˜ ์„ ์ • ์ ˆ์ฐจ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋จผ์ € ์ ์ ˆํ•œ ๋ณ€์ˆ˜ ๊ตฌ๊ฐ„์„ ์„ ์ •ํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜์น˜ํ•ด์„๊ณผ ๊ณ„์ธก ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•œ ๋ณ€์ˆ˜์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํ‰๊ท ๊ณผ ๋ณ€๋™๊ณ„์ˆ˜๋ฅผ ํ†ตํ•ด ์ •ํ™•๋„ ๋ฐ ์ •๋ฐ€๋„๋ฅผ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ, ์ƒ˜ํ”Œ๋ง ์ฃผํŒŒ์ˆ˜ 100Hz์—์„œ ๋ฐ์ดํ„ฐ ๊ธธ์ด 60๋ถ„์— Number of FFT 215 ์ผ ๋•Œ ๊ฐ€์žฅ ์ข‹์€ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ํ–‰์ผˆ ํ–‰๋ ฌ์˜ ํฌ๊ธฐ๋Š” ๊ณ„์‚ฐ๋œ ์ž„ํŽ„์Šค์‘๋‹ตํ•จ์ˆ˜๊ฐ€ ์ตœ๋Œ€๊ฐ’์˜ 50%๋งŒํผ ๋˜์—ˆ์„ ๋•Œ๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๊ฒฐ์ •ํ•˜์˜€๋‹ค. ์‹œ์Šคํ…œ ์ฐจ์ˆ˜์— ๋”ฐ๋ฅธ ์ถ”์ • ๊ฐ์‡ ๋น„์˜ ๋ถ„์‚ฐ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด, ์‹œ์Šคํ…œ ์ฐจ์ˆ˜๋ฅผ 1๋ถ€ํ„ฐ 50๊นŒ์ง€ ๋ฐ”๊ฟ”๊ฐ€๋ฉฐ ์ถ”์ •ํ•œ ๊ฐ์‡ ๋น„์˜ ์ค‘์•™๊ฐ’์„ ์ทจํ•˜์˜€๋‹ค. ์ƒ๊ธฐ์˜ ์ž๋™ํ™”๋œ ๋ณ€์ˆ˜ ์„ ์ • ์ ˆ์ฐจ๋Š” ๊ฒฝํ—˜์  ๋ณ€์ˆ˜์„ ํƒ๋ณด๋‹ค ๋”์šฑ ์ •ํ™•ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ํ˜„์žฅ์‹คํ—˜์„ ํ†ตํ•ด ์ฐจ๋Ÿ‰์œผ๋กœ ์ธํ•ด ์‘๋‹ต์ด ์ง‘์ค‘ํ™”๋˜๋Š” ๊ฒƒ๊ณผ ๊ทธ๊ฒƒ์ด OMA ๊ธฐ๋ฐ˜ ๊ฐ์‡ ๋น„ ์ถ”์ •์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ต๋Ÿ‰์˜ ์ฐจ์„ ์ด ์ ๊ณ  ํ†ตํ–‰๋Ÿ‰์ด ๋งŽ์ง€ ์•Š์„ ๊ฒฝ์šฐ, ์ฐจ๋Ÿ‰์ด ์„ผ์„œ์— ๊ทผ์ ‘ํ•จ์— ๋”ฐ๋ผ ์ƒ์‹œ๊ณ„์ธก ๊ฐ€์†๋„๊ฐ€ ์ปค์ง€๊ณ  ์ฐจ๋Ÿ‰์ด ์„ผ์„œ๋ฅผ ์ง€๋‚˜๊ฐ์— ๋”ฐ๋ผ ์‘๋‹ต์ด ์ค„์–ด๋“ค๊ฒŒ ๋œ๋‹ค. OMA๋Š” ํ•˜์ค‘์„ ์ •์ƒ์ƒํƒœ ๋ฐฑ์ƒ‰์žก์Œ์œผ๋กœ ๊ฐ€์ •ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ด๋Ÿฌํ•œ ์ฐจ๋Ÿ‰์— ์˜ํ•œ ํ•˜์ค‘ ๋ฐ ์‘๋‹ต์˜ ๋น„์ •์ƒ์„ฑ์€ OMA ๋ถˆํ™•์‹ค์„ฑ์˜ ํ•œ ์›์ธ์ด ๋œ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ์ฐจ๋Ÿ‰ ํ•˜์ค‘์€ ๊ณ„์ธก ๊ฐ€์†๋„์˜ ํŒŒ์›Œ์ŠคํŽ™ํŠธ๋Ÿผํ•จ์ˆ˜(Power Spectral Density function, PSD)์˜ ์ฃผํŒŒ์ˆ˜ ์„ฑ๋ถ„์„ ์™œ๊ณกํ•œ๋‹ค. ์ •์ƒ์ƒํƒœ ํ•˜์ค‘ ๋ฐ ์ฐจ๋Ÿ‰ํ•˜์ค‘์— ์˜ํ•œ ๊ณ„์ธก๊ฐ€์†๋„ PSD๋ฅผ ๋น„๊ตํ–ˆ์„ ๋•Œ, ์ฐจ๋Ÿ‰์ด ์ง€๋‚˜๊ฐˆ ๋•Œ 2-5Hz์˜ ๊ณ ์ฃผํŒŒ ์„ฑ๋ถ„์ด ์ฆํญ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ERA๋Š” ์—๋„ˆ์ง€ ๊ธฐ๋ฐ˜์˜ ๊ธฐ๋ฒ•์œผ๋กœ, ์ง€๋ฐฐ์ ์ธ ๋ชจ๋“œ๋ฅผ ๋ณด๋‹ค ์‰ฝ๊ฒŒ ์‹๋ณ„ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด๋Ÿฌํ•œ PSD์˜ ์™œ๊ณก ์—ญ์‹œ ์ถ”์ • ๊ฐ์‡ ๋น„์— ์˜ค๋ฅ˜๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ณ„์ธก ๋ฐ์ดํ„ฐ์˜ ์‹œ๊ณ„์—ด์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋น„์ •์ƒ์„ฑ์„ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด, ์ง„ํญ๋ณ€์กฐํ•จ์ˆ˜๋ฅผ ํ†ตํ•œ ์‹ ํ˜ธ ์ •์ƒํ™” ๊ณผ์ •์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๋น„์ •์ƒ์„ฑ ์‹œ๊ณ„์—ด์€ ํฌ๋ฝํ•จ์ˆ˜์™€ ์ •์ƒ ์‹œ๊ณ„์—ด์˜ ๊ณฑ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ณ„์ธก๋ฐ์ดํ„ฐ์˜ ์ด๋™ Root-Mean-Square(RMS)๋กœ๋ถ€ํ„ฐ ํฌ๋ฝํ•จ์ˆ˜์„ ๊ตฌํ•ด ๊ณ„์ธก ๋ฐ์ดํ„ฐ๋ฅผ ํฌ๋ฝํ•จ์ˆ˜๋กœ ๋‚˜๋ˆ„๋ฉด ๊ณ„์ธก ๋ฐ์ดํ„ฐ ๋‚ด ์ •์ƒ ์‹œ๊ณ„์—ด๋งŒ์„ ์„ฑ๊ณต์ ์œผ๋กœ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. ์ œ์•ˆํ•œ ์‹ ํ˜ธ ์ •์ƒํ™” ๊ณผ์ •์„ 3์ผ๊ฐ„์˜ ์ƒ์‹œ์ง„๋™ ๋ฐ์ดํ„ฐ์— ์ ์šฉํ•˜์—ฌ ๊ฐ์‡ ๋น„๋ฅผ ์ถ”์ •ํ•œ ๊ฒฐ๊ณผ ์ถ”์ •์น˜์˜ ํฐ ํŽธํ–ฅ๋“ค์ด ์ œ๊ฑฐ๋˜์—ˆ๊ณ , ๋ณ€๋™๊ณ„์ˆ˜ ์—ญ์‹œ ์ค„์–ด๋“ค์—ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์‹ ํ˜ธ ์ •์ƒํ™” ๊ณผ์ •์ด ๊ฐ์‡ ๋น„์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ์ œ๊ฑฐํ•˜๋Š”๋ฐ ์œ ํšจํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋Œ€์ƒ๊ต๋Ÿ‰์— MTMD๊ฐ€ ์„ค์น˜๋œ ์ดํ›„์˜ ๊ฐ์‡ ๋น„๋ฅผ ๋™์ผํ•œ ๋ฐฉ์‹์œผ๋กœ ์ถ”์ •ํ•˜์˜€๋‹ค. ๋‹ค์–‘ํ•œ ๋ฐ”๋žŒ ์กฐ๊ฑด ํ•˜์—์„œ ๊ณ„์ธก๋œ 4์ผ๊ฐ„์˜ ๋ฐ์ดํ„ฐ๋ฅผ ํ†ตํ•ด ๊ฐ์‡ ๋น„๋ฅผ ์ถ”์ •ํ•œ ๊ฒฐ๊ณผ, ์—ญ์‹œ ์ž๋™ํ™”๋œ ๋ณ€์ˆ˜ ์„ ํƒ ๋ฐ ์‹ ํ˜ธ ์ •์ƒํ™” ๊ณผ์ •์ด OMA ๊ธฐ๋ฐ˜ ์ถ”์ • ๊ฐ์‡ ๋น„์˜ ๋ถˆํ™•์‹ค์„ฑ์„ ์ค„์—ฌ์ฃผ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํŠน๋ณ„ํžˆ MTMD์— ์˜ํ•œ ๊ฐ์‡ ๋น„ ์ฆ๊ฐ€ ํšจ๊ณผ๋Š” ํ’์†์ด ์™€๋ฅ˜์ง„๋™ ์œ„ํ—˜ํ’์†์ผ ๊ฒฝ์šฐ ๊ฐ€์žฅ ์ปธ์œผ๋ฉฐ, ์ผ๋ฐ˜์ ์ธ ํ’์† ์กฐ๊ฑด์—์„œ๋Š” ๋น„๋ก ์„ค๊ณ„์ˆ˜์ค€์˜ ์„ฑ๋Šฅ์„ ๋ณด์ด์ง€๋Š” ์•Š์•˜์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ถฉ๋ถ„ํ•œ ์ œ์ง„ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ํ™˜๊ฒฝ์  ์š”์ธ์ด ๊ฐ์‡ ๋น„ ๋ณ€ํ™”์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ง„ํญ, ์ฐจ๋Ÿ‰ ๋Œ€์ˆ˜ ๋ฐ ์˜จ๋„๋ฅผ ๋Œ€์ƒ ๋ณ€์ˆ˜๋กœ ์„ ์ •, ์ƒ๊ด€์„ฑ ๋ถ„์„์„ ํ†ตํ•ด ๊ฐ ํ™˜๊ฒฝ์  ์š”์ธ๊ณผ ๊ฐ์‡ ๋น„ ์‚ฌ์ด์˜ ๊ด€๋ จ์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ฐจ๋Ÿ‰์— ์˜ํ•œ ์ง„๋™์ด ์ผ€์ด๋ธ” ๊ต๋Ÿ‰ ์ง„๋™ ๋ฐœํ˜„์— ๊ฐ€์žฅ ํฐ ์š”์ธ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์™€๋ฅ˜์ง„๋™ Lock-in ๊ตฌ๊ฐ„์—์„œ๋„ ์ง„ํญ์ด ์ฆ๊ฐ€ํ•˜์˜€์œผ๋‚˜, ์ด๋Š” ๊ฐ์‡ ๋น„ ์ฆ๊ฐ€์—๋Š” ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์˜จ๋„์™€์˜ ์ƒ๊ด€์„ฑ ์—ญ์‹œ ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์•˜๋‹ค. ๋Œ€ํ‘œ ๋ณ€์ˆ˜์ธ ์ฐจ๋Ÿ‰์— ์˜ํ•œ ์ง„ํญ ์˜์กด๋„(amplitude-dependency)๋ฅผ ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์‹ ํ˜ธ ์ •์ƒํ™” ๊ณผ์ •์„ ์ ์šฉํ•˜๊ธฐ ์ „์—๋Š” ๊ฑฐ์˜ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š๋˜ ์ƒ๊ด€์„ฑ์ด ์‹ ํ˜ธ ์ •์ƒํ™” ์ ์šฉ์— ๋”ฐ๋ผ ๋ช…ํ™•ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.An enhanced output-only algorithm of the Natural Excitation Techniqueโ€”Eigensystem Realization Algorithm (NExT-ERA) was suggested to solve a large scattering in identified damping ratio still remains to be a challenging issue. The suggested damping estimation procedure was applied to a parallel cable-stayed bridge for identifying structural damping ratios before and after the installation of a Multiple Tuned Mass Damper (MTMD) designed to mitigate a vortex-induced vibration (VIV) that was observed on the bridge. The automated proper parameter selection process for NExT-ERA was suggested to reduce the large error bound due to poor parameter selection and achieve similar level of correctness of heuristic parameter selection. To make suitable criteria for each algorithm parameter, a series of parametric studies using numerical simulation and operational monitoring data was performed, respectively. It was discovered that the number of FFT (NFFT) of 215 with 60 min data provided the accurate estimation in terms of mean and coefficient of variance (COV). A size of Hankel matrix was determined corresponding to the shape of calculated impulse response function (IRF). To overcome the limitation of non-structural model based algorithm, the sensitivity analysis was performed using each estimated value according to a system order. The median value of estimated damping ratios provided a converged value successfully. This automated parameter selection process accomplished the more accurate damping estimation compared to the result of heuristic parameter selection. The research also discovered that the effect of traffic loadings on the uncertainty of operational modal analysis (OMA) based damping estimation. The experimental studies found a localized response in traffic-induced vibration (TIV). When the number of traffic lane over the bridge is only one or two for one direction and the traffic volume is not high, the ambient vibration signal at the sensor position show an envelope as a vehicle is approaching and fading away. Since OMA assumes that the signal to be analyzed is a stationary white-noise process, the envelop-like signal obtained from running vehicles can also contribute the scattering in Structural Identification (St-Id). Furthermore, this traffic loading distorted the PSD of measured acceleration. A comparison between the PSD of stationary response and TIV clearly demonstrated that the frequency components of 2-5 Hz were amplified during the vehicle crossed the bridge. The ERA is the energy-based method so that the dominant modes will be more easily identified. Therefore, the distortion in PSD can be a reason for the poor modal identification. To remove this envelope-like trend in measured data, a signal stationarization process based on amplitude-modulating function was employed to the simulated response and measured data, respectively. If the nonstationary loading can be represented by the product of amplitude-modulating function and stationary white noise process, then the envelope function can be evaluated by temporal root-mean-square function of responses. Consequently, the approximated stationary process can be extracted by dividing the measurements with calculated envelope function. This signal stationarization process successfully extracted the stationary process from the TIV. This signal stationarization process was applied to the 3-day operational monitoring data. It is discovered that some highly scattered points were eliminated by the signal stationarization. The COV of estimated damping ratios is significantly reduced, indicating the signal stationarization process is worth in reducing scattering in identified damping ratios from OMA. As a result, the amplitude dependency of a damping ratio also more clearly appeared in terms of R-squared value of linear regression increasing. Since a multiple tuned mass damper (MTMD) was installed at the center of the main span to mitigate VIV, the modal damping ratios before and after the installation of the MTMD were also compared. Several sets of operational monitoring data that had been collected under various windy conditions were used to develop a relationship between the identified damping ratios and the corresponding VIV level of the bridge. The performance of the bridge was enhanced regarding vibrational serviceability, based on the above findings. The effect of environmental factors on the variation of damping ratio was evaluated. Three environmental factors of vibration amplitude, the number of vehicle and temperature were selected, and the statistical relationship between the damping ratio and each environmental parameter was evaluated through correlation analysis. The result confirmed that the main source of damping ratio of cable-supported bridge was traffic-induced vibration which showed a high positive correlation with the number of vehicle and corresponding vibration amplitude of TIV. RMS amplitude also increased within lock-in range, but no corresponding increase in the damping ratio was observed. The effect of temperature changes was also relatively low. The analysis of the amplitude-dependency of the damping ratio clearly showed that a tendency was clearly appeared by applying the stationarization method to reducing uncertainties in damping estimates.1. Introduction 1 1.1. Research background 3 1.2. Problem definition 7 1.3. Objective and scope 9 2. Damping estimation based on operational modal analysis 13 2.1. NExT-ERA: Output-only operational modal analysis 15 2.1.1. Literature survey: OMA-based damping estimation 15 2.1.2. Natural Excitation Technique (NExT) 17 2.1.3. Eigensystem Realization Algorithm (ERA) 23 2.2. Damping estimation of cable-stayed bridge 29 2.2.1. Bridge description: Jindo Bridge 29 2.2.2. Monitoring data 30 2.2.3. Excitation test using TMD 33 2.2.4. Operational damping estimation using 3-days data 38 2.3. Concluding remarks 42 3. Automated proper parameter selection for NExT-ERA 43 3.1. Analysis parameter of NExT-ERA 45 3.2. Parametric studies of numerical simulation 46 3.2.1. Description of the simulated models 46 3.2.2. Algorithmic parameters of NExT: NFFT and data length 47 3.2.3. Algorithmic parameters of ERA: Size of Hankel matrix and system order 53 3.3. Parametric studies using field monitoring data 58 3.3.1. NFFT and the record length 59 3.3.2. Size of Hankel matrix 60 3.3.3. System order 61 3.4. Application: Jindo Bridge 63 3.4.1. Parameter selection 63 3.4.2. Estimated damping ratio after proper parameter selection 66 3.4.3. Computational cost 68 4. Signal stationarization for traffic-induced vibration 71 4.1. Introduction 73 4.2. Experimental investigation for TIV properties 75 4.2.1. Bridge description: Sorok Bridge 75 4.2.2. Monitored data 76 4.2.3. Experimental condition 77 4.2.4. Nonstationary effect of TIV on a measured signal 80 4.3. Signal stationarization using amplitude-modulating function 85 4.3.1. Amplitude-modulating function 85 4.3.2. The optimal segment length for signal stationarization 87 4.3.3. Signal stationarization of operational monitoring data 91 4.4. Application to NExT-ERA: numerical simulation 93 4.4.1. Description of simulated models 93 4.4.2. Estimated damping ratio corresponding to signal stationarization 100 4.5. Application to NExT-ERA: operational monitoring data of Jindo Bridge 102 4.5.1. Combined framework of p.p.s and stationarization 102 4.5.2. Monitoring data 104 4.5.3. Effect of signal stationarization in Jindo Bridge 104 4.5.4. Estimated damping ratio according to signal stationarization 107 4.6. Application to NExT-ERA: Jindo Bridge after installation of MTMD 112 4.6.1. Monitored data 112 4.6.2. Estimated damping ratio corresponding to signal stationarization 114 4.6.3. Effectiveness of MTMD according to windy condition 118 4.7. Concluding remarks 126 5. Environmental effect on the variation of estimated damping ratio 127 5.1. Main environmental factors for variation of damping ratio 130 5.1.1. Vibration amplitude 130 5.1.2. Number of vehicle 132 5.1.3. Temperature 133 5.1.4. Aerodynamic damping ratio 135 5.2. Correlation analysis between estimated damping ratio and environmental factors 138 5.3. Correlation analysis between estimated damping ratio and environmental factors without lock-in range data set 141 5.4. Amplitude-dependency 143 5.5. Conclusion 146 6. Conclusions and further study 147 6.1. Conclusions and contributions 149 6.2. Suggestion 151 References 154Docto

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