• Smart Structures and Systems
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• v.33 no.3
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.1-13
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• 2024

The construction adit plays a pivotal role in enhancing the working face during the excavation of long-distance and deep hydraulic tunnels. However, the intersection zone between the main tunnel and the construction adit exhibits more intricate deformation patterns in surrounding rock, posing a significant threat to stability during excavation. Taking the Xianglushan tunnel in Yunnan Province, China, as a case study, the FLAC3D software is employed to simulate the excavation process at the intersection. The simulation results are verified combined with the field deformation monitoring results, and the spatial distribution of tunnel rock deformation in the intersection area are analyzed. Five excavation conditions with different intersection angles are simulated, and the surrounding rock deformation of the tunnel intersection area with different intersection angles is analyzed, and its influence range is discussed. The results show that: (1) The surrounding rock deformation in the intersection area increases rapidly during the tunnel excavation. With the increase of construction distance, the deformation of intersection area is gradually stable. (2) The deformation distribution of the tunnel rock is uneven, and the deformation of main tunnel near the intersection area is larger than that far away from the intersection area. (3) With the increase of the intersection angle, the surrounding rock deformation of the tunnel intersection and its influence range decreases gradually. The research results have certain guiding significance for the construction safety of the tunnel intersection area.

• Smart Structures and Systems
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• v.20 no.5
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.

• Structural Monitoring and Maintenance
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• v.5 no.2
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• pp.243-260
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• 2018

Detection of damages in fibre reinforced plastic (FRP) composite structures is important from the safety and serviceability point of view. Usually, damage is realized as a local reduction of stiffness and if dynamic responses of the structure are sensitive enough to such changes in stiffness, then a well posed inverse problem can provide an efficient solution to the damage detection problem. Usually, such inverse problems are solved within the framework of pattern recognition. Support Vector Machine (SVM) Algorithm is one such methodology, which minimizes the weighted differences between the experimentally observed dynamic responses and those computed using the finite element model- by optimizing appropriately chosen parameters, such as stiffness. A damage detection strategy is hereby proposed using SVM which perform stepwise by first locating and then determining the severity of the damage. The SVM algorithm uses simulations of only a limited number of damage scenarios and trains the algorithm in such a way so as to detect damages at unknown locations by recognizing the pattern of changes in dynamic responses. A rectangular fiber reinforced plastic composite plate has been investigated both numerically and experimentally to observe the efficiency of the SVM algorithm for damage detection. Experimentally determined modal responses, such as natural frequencies and mode shapes are used as observable parameters. The results are encouraging since a high percentage of damage cases have been successfully determined using the proposed algorithm.

• Earthquakes and Structures
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• v.18 no.3
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• pp.321-335
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• 2020

The aim of the present contribution is to consider and underline the essential interactions among the historical knowledge, the seismic vulnerability assessment, the investigation experimental tools, the preservation of the architectural quality and the strengthening design in regard to architectural heritage conservation. These topics are argued in relation to Palazzo Murena in Perugia, designed in the eighteenth century by the famous Architect Luigi Vanvitelli, and currently headquarters of the city's University. Based on the surveys and the visual inspections, a preliminary a priori global analysis has been performed by means of the FME method. The obtained results permitted to plan an experimental tests campaign inclusive of structural health monitoring. The new achieved "knowledge" of the building allowed to refine the seismic safety assessment. In particular it was highlighted that the "mezzanine floor" can be a vulnerable element of the building with the collapse of its masonry walls. Preserving the architectural characteristics, a local reinforcement intervention is proposed for the above-mentioned level; this consists of the application of plaster with FRCM, assuring an adequate strength, without burden the masonry structure with additional weight, and therefore a decreasing of the seismic vulnerability. The necessity to consider, in this ongoing research, other local mechanisms is highlighted in the unfolding of the last part of work.

• Korean Journal of Occupational Health Nursing
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• v.29 no.1
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• pp.78-87
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• 2020

Purpose: The purpose of this study was to identify the relationship between nursing error experience, coping behavior and job stress in operating room. Methods: A descriptive research design was used in this study. The participants were 228 operating room nurses in G city who surveyed between October 25 and November 25, 2017 using self-report questionnaires. The data were analyzed using IBM SPSS/WIN 24.0/AMOS WIN 24.0 Program, which determined frequency, percentage, mean, standard deviation, Pearson correlation coefficient, and structural equation model. Results: There were significant positive correlations between six sub-categories of nursing errors and job stress. We found negative correlations between coping behavior and job stress. There was a mediating effect of active coping between knowledge of nursing error and job stress. We found passive coping between inspection & monitoring related error and job stress. Conclusion: Study findings suggest that adequate education and the improvement in hospital environment and system should be required to reduce the nurses' job stress related to the patients' safety in operating room.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.264-270
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• 1998

This research broadens the prime concern of nuclear power plant operations from safe performance to both economic and safe performance. First emergency diesel generator is identified as one of main contributors for the lost plant availability through the review of plants forced outage records. The framework of an integrated architecture for performing modern on-line condition for operational availability improvement is configured in this work. For the development of the comprehensive sensor networks for complex target systems, an integrated methodology incorporating a structural hierarchy, a functional hierarchy, and a fault-system matrix is formulated. The second part of our research is development of intelligent diagnosis and maintenance advisory system, which employs Bayesian Belief networks (BBNs) as a high level reasoning tool incorporating inherent uncertainty use in probabilistic inference. Our prototype diagnosis algorithms are represented explicitly through topological symbols and links between them in a causal direction. As new evidence from sensor network development is entered into the model especially, our advisory of system provides operational advice concerning both availability and safety, so that the operator is able to determine the likely modes, diagnose the system state, locate root causes, and take the most advantageous action. Thereby, this advice improves operational availability

• Structural Engineering and Mechanics
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• v.69 no.5
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• pp.557-566
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• 2019

The reservoir basin bedrock produced significant impact on the long-term service safety of super-high arch dams. It was important for accurately identifying geomechanical parameters and its evolution process of reservoir basin bedrock. The deformation modulus mechanism research methods of reservoir basin bedrock deformation modulus for super-high arch dams was carried out by finite element numerical calculation of the reservoir basin bedrock deformation and in-situ monitoring data analysis. The deformation modulus inversion principle of reservoir basin bedrock in a wide range was studied. The convergence criteria for determining the calculation range of reservoir basin of super-high arch dams was put forward. The implementation method was proposed for different layers and zones of reservoir basin bedrock. A practical engineering of a super-high arch dam was taken as the example.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Structural Monitoring and Maintenance
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• v.10 no.4
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• pp.361-379
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• 2023

In cable structure maintenance, particularly for cable-stayed bridges, cable safety assessment relies on estimating cable tension. Conventionally, in Japan, cable tension is estimated from the natural frequencies of the cable using the higher-order vibration method. In recent years, dampers have been installed on cables to reduce cable vibrations. Because the higher-order vibration method is a method for damper-free cables, the damper must be removed to measure the natural frequencies of a cable without a damper. However, cables on some cable-stayed bridges have two dampers: one on the girder side and another on the tower side. Notably, removing and reinstalling the damper on the tower side are considerably more time- and labor-intensive. This paper introduces a tension estimation method for cables with two dampers, using natural frequencies. The proposed method was validated through numerical simulation and experiment. In the numerical tests, without measurement error in the natural frequencies, the maximum estimation error among 100 models was 3.3%. With measurement error of 2%, the average estimation error was within 5%, with a maximum error of 9%. The proposed method has high accuracy because the higher-order vibration method for a damper-free cable still has an estimation error of 5%. The experimental verification emphasizes the importance of accurate damper modeling, highlighting potential discrepancies between existing damper design formula and actual damper behavior. By revising the damper formula, the proposed method achieved accurate cable tension estimation, with a maximum estimation error of approximately 10%.