• Journal of Construction Engineering and Project Management
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• v.1 no.2
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• pp.1-10
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• 2011

Bridges are critical and valuable components in any road and rail transportation network. Therefore bridge remediation has always been a top priority for asset managers and engineers, but identifying the nature of true defect deterioration and associated remediation treatments remains a complex task. Nowadays Decision Support Systems (DSS) are widely used to assist decision makers across an extensive spectrum of unstructured decision environments. The main objective of this research is to develop a requirements-driven methodology for bridge monitoring and maintenance which has the ability to assess the bridge condition and find the best remediation treatments using Simple Multi Attribute Rating Technique (SMART); with the aim of maintaining a bridge within acceptable limits of safety, serviceability and sustainability.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.53-59
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• 2021

The marine transport industry generally applies new technologies later than other transport industries, such as airways and railways. Vessels require efficient operation, and their performance and lifespan depend on the level of maintenance and management. Many studies have shown that corrective maintenance (CM) and time-based maintenance (TBM) have restrictions with respect to enabling efficient maintenance of workload and cost to improve operational efficiency. Predictive maintenance (PdM) is an advanced technology that allows monitoring the condition and performance of a target machine to predict its time of failure and helps maintain the key machinery in optimal working conditions at all times. This study presents the development of a marine predictive maintenance (MPdM; maritime predictive maintenance) method based on applying PdM to the marine environment. The MPdM scheme is designed by considering the special environment of the marine transport industry and the extreme marine conditions. Further, results of the study elaborates upon the concept of MPdM and its necessity to advancing marine transportation in the future.

• The KSFM Journal of Fluid Machinery
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• v.12 no.5
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• pp.39-46
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• 2009

This paper outlines how the on-line performance monitoring system can be used to improve the efficiency and maintenance of the equipments. And a method of the heat rate allocation to each equipment was suggested to monitor the performance of combined cycle power plants. This calculates the expected heat rate of current conditions and compares it with actual values. Loss allocation in heat rate is reconciled by calculating the magnitude of the deficiency contributed by major components, such as the gas turbine, heat recovery steam generator, steam turbine and condenser. Expected power output is determined by a detailed model and correction curves of the plant. This simulation models are found to reproduce high accuracy in behavior of the cycle for various operating conditions, both in design and in off-design condition. Errors are lower than 2% in most cases.

• Smart Structures and Systems
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• v.30 no.6
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• pp.557-569
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• 2022

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.107-116
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• 2023

The health condition of of deep water high pile foundation is vital to the safe operation of bridges. However, pier foundations are vulnerable to damage in deep water due to exposure to sea torrents and corrosive environments over an extended period. In this paper, combined with aninvestigation and analysis of the typical damage characteristics of main pier group pile foundations, we study the safety monitoring and real-time early warning technology of the deep water high pile foundations, we propose an early warning index item and early warning threshold of deep water high pile foundation by utilizing a numerical simulation analysis and referring to domestic and foreign standards and literature. First, we combine the characteristics of structures and draw on more mature evaluation theories and experience in civil engineering-related fields such as dam and bridge engineering. Then, we establish a scheme consisting of a Early Warning Index Systemand evaluation model based on the analytic hierarchy process and constant weight evaluation method and apply the research results to a project based on the Jiashao bridge in Zhejiang province, China. Finally, we verify the rationality and reliability of the Early Warning Index Systemof the Deep Water High Pile Foundations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.2
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• pp.63-72
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• 2016

An expected-discounted cost model based on RRP(Renewal Reward Process), referred to as a stochastic decision model, has been developed to estimate the optimal period of in-depth inspection which is one of critical issues in the life-cycle maintenance management of harbor structures such as rubble-mound breakwaters. A mathematical model, which is a function of the probability distribution of the service-life, has been formulated by simultaneously adopting PIM(Periodic Inspection and Maintenance) and CBIM(Condition-Based Inspection and Maintenance) policies so as to resolve limitations of other models, also all the costs in the model associated with monitoring and repair have been discounted with time. From both an analytical solution derived in this paper under the condition in which a failure rate function is a constant and the sensitivity analyses for the variety of different distribution functions and conditions, it has been confirmed that the present solution is more versatile than the existing solution suggested in a very simplified setting. Additionally, even in that case which the probability distribution of the service-life is estimated through the stochastic process, the present model is of course also well suited to interpret the nonlinearity of deterioration process. In particular, a MCS(Monte-Carlo Simulation)-based sample path method has been used to evaluate the parameters of a damage intensity function in stochastic process. Finally, the present stochastic decision model can satisfactorily be applied to armor units of rubble mound breakwaters. The optimal periods of in-depth inspection of rubble-mound breakwaters can be determined by minimizing the expected total cost rate with respect to the behavioral feature of damage process, the level of serviceability limit, and the consequence of that structure.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.181-197
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• 2015

Assessing the condition of paint on civil structures is an important but challenging and costly task, in particular when it comes to large and complex structures. Current practices of visual inspection are labour-intensive and time-consuming to perform. In addition, this task usually relies on the experience and subjective judgment of individual inspectors. In this study, hyperspectral imaging and classification techniques are proposed as a method to objectively assess the state of the paint on a civil or other structure. The ultimate objective of the work is to develop a technology that can provide precise and automatic grading of paint condition and assessment of degradation due to age or environmental factors. Towards this goal, we acquired hyperspectral images of steel surfaces located at long (mid-range) and short distances on the Sydney Harbour Bridge with an Acousto-Optics Tunable filter (AOTF) hyperspectral camera (consisting of 21 bands in the visible spectrum). We trained a multi-class Support Vector Machines (SVM) classifier to automatically assess the grading of the paint from hyperspectral signatures. Our results demonstrate that the classifier generates highly accurate assessment of the paint condition in comparison to the judgement of human experts.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.319-344
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• 2020

Inverse analysis of non-linear reinforced concrete bridge pier using recursive Gaussian filtering for in-situ condition assessment is the main theme of this work. For this purpose, minimum variance unbiased estimation using unscented sigma points is adopted here. The uniqueness of this inverse analysis lies in its approach for strain based updating of engineering demand parameters, where appropriate bound and constrained conditions are introduced to ensure numerical stability and convergence. In this analysis, seismic input is also identified, which is an added advantage for the structures having no dedicated sensors for earthquake measurement. First, the proposed strategy is tested with a simulated example whose hysteretic properties are obtained from the slow-cyclic test of a frame to investigate its efficiency and accuracy. Finally, the experimental test data of a full-scale bridge pier is used to study its in-situ condition in terms of Park & Ang damage index. Overall the study shows the ability of the augmented minimum variance unbiased estimation based recursive time-marching algorithm for non-linear system identification with the aim to estimate the engineering damage parameters that are the fundamental information necessary for any future decision making for retrofitting/rehabilitation.

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.88-95
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• 1998

Vibration analysis of rotating machinery can give an indication of possible faults thus allowing maintenance before further damage occurs. Current predictive maintenance system installed in Pyung-tak has the ability to diagnose the mechanical problems within the LNG Pump when the vibration exceeds preset overall alarm levels. In this study, LNG pump auto-diagnosis system based upon Windows NT and DSP Board is developed. This system analysis velocity signal acquired from dual accelerometer input monitor system to diagnose pump condition. Many plots which display machine condition are shown and features of vibration are stored in every time. If the fault is found, the system diagnoses automatically using expert system and trend monitoring. Operator checks pump condition intuitively using personal computer monitor.

• Structural Monitoring and Maintenance
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• v.1 no.4
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• pp.357-369
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• 2014

In the existing bridge management systems, assessment of the structural behavior is based on the results of visual inspections in which corresponding condition states are assigned to individual elements. In this process, limited attention is given to the correlation between bridge elements from structural perspective. Also, the uncertainty of parameters which affect the structural capacity is ignored. A system reliability-based assessment model is potentially an appropriate replacement for the existing procedures. The aim of this research is to evaluate the system reliability of existing conventional Steel-Reinforced bridge decks over time. The developed method utilizes the reliability theory and evaluates the structural safety for such bridges based on their failure mechanisms. System reliability analysis has been applied to simply-supported concrete bridge superstructures designed according to the Canadian Highway Bridge Design Code (CHBDC-S6) and the deterioration pattern is achieved based on the reliability estimates. Finally, the bridge condition index of an old existing bridge in Montreal has been estimated using the developed deterioration pattern. The results obtained from the developed reliability-based deterioration model and from the evaluation done by bridge engineers have been found to be in accordance.