• Smart Structures and Systems
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• 제26권1호
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• pp.35-47
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• 2020

This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.

• Smart Structures and Systems
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• 제30권6호
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• pp.583-599
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• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2008년도 춘계 학술발표회 제20권1호
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• pp.201-204
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• 2008

AASHTO LRFD의 단면설계법은 일반적인 교량 거더, 슬래브와 일반적인 보이론의 가정이 유효한 기타 부재영역에 적용이 가능하다. 콘크리트 부재의 전단저항은 콘크리트의 인장응력에 기초한 콘크리트전단성분 $V_c$와 횡방향 철근의 인장응력에 기반한 전단철근의 전단성분 $V_s$로 구분할 수 있다. 스트레싱과 비스트레싱 부재 모두 $V_c$와 $V_s$ 항은 작용하중과 단면 성질에 근거한 ${\beta}$와 ${\theta}$의 항으로 적용된다. ${\beta}$가 2이고 ${\theta}$가 45$^{\circ}$ 일 경우, 전단강도는 전단저항을 평가하는 전통적 방법과 근본적으로 동일하다. 그러나 최근 대규모 실험결과, 이러한 전통적 방법이 횡방향철근을 포함하지 않는 대형부재에서 심각히 불안전하다고 알려져 있다. 본 연구에서는 AASHTO LRFD의 전단설계기준을 살펴보고 문제점을 고찰한 것이다.