• Smart Structures and Systems
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• v.26 no.1
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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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• v.30 no.6
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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.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.201-204
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• 2008

The Sectional Design Model(AASHTO LRFD) is appropriate for the design of typical bridge girders, slabs, and other regions of components where the assumptions of traditional engineering beam theory are valid. The shear resistance of a concrete member may be separated into a component, $V_c$, that relies on tensile stresses in the concrete, $V_s$, that relies on tensile stresses in the transverse reinforcement. The expressions for $V_c$ and $V_s$ apply to both prestressed and nonprestressed section, with the terms ${\beta}$ and ${\theta}$ depending on the applied loading(M, V, N, and T) and the properties of the section. With ${\beta}$ taken as 2.0 and ${\theta}$ as 45$^{\circ}$, the expressions for shear strength become essentially identical to those traditionally used for evaluating shear resistance. Recent large-scale experiments, however, have demonstrated that these traditional expression can be seriously unconservative for large members not containing transverse reinforcement. And This paper can present only a brief introduction to shear design of AASHTO LRFD and is to review of the technical difficulty.