러브파와 레일리파의 분산특성을 이용한 동시역산해석(I) - 동시역산해석기법의 구성 -

Joint Diversion Analysis Using the Dispersion Characteristics of Love Wave and Rayleigh Wave (I) - Constitution of Joint Diversion Analysis Technique -

  • 이일화 (한국철도기술연구원 궤도토목연구본부) ;
  • 조성호 (중앙대학교 건설대학 토목공학과)
  • 발행 : 2005.06.01

초록

러브파와 레일리파는 표면파로서 각 파가 가지는 분산특성을 활용하여 지반의 강성주상도를 파악할 수 있는 특징을 가지고 있다. 이 중 러브파는 한 방향에 대한 응력-변위만 고려하면 되기 때문에 수치적 모델링이 간단하다. 전파시에는 이론적으로 체적파의 영향 및 밀도의 변화가 없어 각 각의 물성치를 갖는 다층구조지반에서 적용성이 높다고 할 수 있다. 이러한 장점을 활용하여 최종적으로는 단파장에 유리한 러브파와 장파장에 유리한 레일리파의 분산정보를 같이 이용하여 동시역산해석을 할 수 있는 기법을 제안하고자 한다. 해석기법은 크게 3가지로 구성되는데, 러브파와 레일리파의 정모델링 해석을 위해 전달행렬법의 구성, 대상 지반의 시스템 결정을 위한 민감도행렬의 구성 그리고 역산해석기법인 Damped Least Square Solution (DLSS)이다. 동시역산의 방법은 민감도행렬을 구성할 때 레일리파와 러브파의 분산정보가 동시에 사용되며, 계산된 민감도행렬을 역산해석하여 지반의 전단파 주상도를 반복적으로 계산하게 된다. 동시역산해석의 목적은 각 파의 주파수 기여도가 다른 점을 활용함으로서 역산결과의 정확도와 수렴도를 향상하고자 함이다.

Love wave and Rayleigh wave are the major elastic waves belonging to the category of the surface wave. Those waves are used to determine the ground stiffness profile using their dispersion characteristics. The fact that Love wave is not contaminated by P-wave makes Love wave superior to Rayleigh wave and other body waves. Therefore, the information that Love wave carries is more distinct and clearer than that of others. Based on theoretical research, the joint inversion analysis that uses the dispersion information of both Love and Rayleigh wave was proposed. This analysis consists of the forward modeling using transfer matrix, the sensitivity matrix for evaluating the ground system and DLSS (Damped Least Square Solution) as an inversion technique. The technique of joint inversion uses the dispersion characteristics of Love wave and Rayleigh wave simultaneously making the sensitivity matrix. The sensitivity matrix was used for inversion analysis repeatedly to find the approximate ground stiffness profile. The purpose of the joint inversion analysis is to improve accuracy and convergency of inversion results by utilizing that frequency contribution of each wave is different.

키워드

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