• Smart Structures and Systems
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• v.5 no.4
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• pp.357-367
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• 2009

Structural Health Monitoring (SHM), particularly remote monitoring, is an emerging field with great potential to help infrastructure owners obtain more and up-to-date knowledge of their structures. The methodology could provide supplemental information to guide the frequency and extent of visual inspections, and the possible need for maintenance. The instrumentation for a SHM system needs to be developed with longevity and the objectives for the system in mind. Sensors need to be selected for reliability and durability, sited where they provide the maximum information for the objectives, and where they can be accessed and replaced should the need arise over the monitoring period. With the rapid changes now occurring with sensors and software, flexibility needs to be in place to allow the system to be upgraded over time. Damage detection needs to be considered in terms of the type of damage that needs to be detected, informing maintenance requirements, and how detection can be achieved. Current vibration analysis techniques appear not yet to have achieved the necessary sensitivity for that purpose. Societal factors will influence the design of a SHM system in terms of the sophistication of the instrumentation and methodology employed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1157-1163
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• 2014

Needs for a new technologies of infrastructure systems arose, following the development of next generation EMU(Electric Multiple Unit) train with maximum speed over 400km/h. For high-speed operation tests of the new EMU, a high-speed railway infrastructure test-bed was constructed in a 28km long section of the Honam High-speed Railway. Diverse sensors and monitoring system was installed for continuous monitoring of the railway. Due to such effort, further demands and needs of the integrated monitoring system was derived in a more comprehensive and long-term perspective.

• Smart Structures and Systems
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• v.33 no.3
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• pp.227-241
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• 2024

Safety and structural integrity of civil structures, like bridges and buildings, can be substantially enhanced by employing appropriate structural health monitoring (SHM) techniques for timely diagnosis of incipient damages. The information gathered from health monitoring of important infrastructure helps in making informed decisions on their maintenance. This ensures smooth, uninterrupted operation of the civil infrastructure and also cuts down the overall maintenance cost. With an early warning system, SHM can protect human life during major structural failures. A real-time online damage localization technique is proposed using only the vibration measurements in this paper. The concept of the 'Degree of Scatter' (DoS) of the vibration measurements is used to generate a spatial profile, and fractal dimension theory is used for damage detection and localization in the proposed two-phase algorithm. Further, it ensures robustness against environmental and operational variability (EoV). The proposed method works only with output-only responses and does not require correlated finite element models. Investigations are carried out to test the presented algorithm, using the synthetic data generated from a simply supported beam, a 25-storey shear building model, and also experimental data obtained from the lab-level experiments on a steel I-beam and a ten-storey framed structure. The investigations suggest that the proposed damage localization algorithm is capable of isolating the influence of the confounding factors associated with EoV while detecting and localizing damage even with noisy measurements.

• Geomechanics and Engineering
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• v.24 no.4
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• pp.371-388
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• 2021

A novel flexibility-based beam-foundation model for inelastic analyses of beams resting on foundation is presented in this paper. To model the deformability of supporting foundation media, the Winkler-Pasternak foundation model is adopted. Following the derivation of basic equations of the problem (strong form), the flexibility-based finite beam-foundation element (weak form) is formulated within the framework of the matrix virtual force principle. Through equilibrated force shape functions, the internal force fields are related to the element force degrees of freedom. Tonti's diagrams are adopted to present both strong and weak forms of the problem. Three numerical simulations are employed to assess validity and to show effectiveness of the proposed flexibility-based beam-foundation model. The first two simulations focus on elastic beam-foundation systems while the last simulation emphasizes on an inelastic beam-foundation system. The influences of the adopted foundation model to represent the underlying foundation medium are also discussed.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1221-1245
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• 2014

Real-time hybrid simulation (RTHS) is a promising cyber-physical technique used in the experimental evaluation of civil infrastructure systems subject to dynamic loading. In RTHS, the response of a structural system is simulated by partitioning it into physical and numerical substructures, and coupling at the interface is achieved by enforcing equilibrium and compatibility in real-time. The choice of partitioning parameters will influence the overall success of the experiment. In addition, due to the dynamics of the transfer system, communication and computation delays, the feedback force signals are dependent on the system state subject to delay. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In light of this, guidelines should be established to facilitate successful RTHS and clearly specify: (i) the minimum requirements of the transfer system control, (ii) the minimum required sampling frequency, and (iii) the most effective ways to stabilize an unstable simulation due to the limitations of the available transfer system. The objective of this paper is to establish a stability switch criterion due to systematic experimental errors. The RTHS stability switch criterion will provide a basis for the partitioning and design of successful RTHS.

• Journal of Wetlands Research
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• v.10 no.3
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• pp.37-45
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• 2008

The Philippine government enacted the National Water Crisis Act in 1995, as a response to the burgeoning situation of water supply systems in the country. This act led to the privatization of Metropolitan Waterworks and Sewerage System (MWSS), sector having jurisdiction and control over all waterworks and sewerage systems in a service area including Metro Manila. Nowadays, the region's supply of water is still facing a lot of difficulties, both in quality and quantity. The unabated migration of people to the metro which increases its population, tapping from the aged pipelines, lack of water facilities and infrastructure, excessive groundwater withdrawal, environmental degradation, and surface and groundwater pollution are some of the issues that Metro Manila have to deal with. These situations lead to two primary water supply issues suffered by Metro Manila: water shortage and flooding. The purpose of this paper was to present water supply in Metro Manila with respect to the problems in its distribution, environmental implications and quality. In this paper, several technical reports, published literature, and news articles were consulted and became the major basis for identifying gaps and suggesting remedial measures.

• Smart Structures and Systems
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• v.32 no.4
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• pp.235-251
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• 2023

A method that can estimate global deformation and internal forces using a limited amount of displacement data and based on the shape superposition technique and a neural network has been recently developed. However, it is difficult to directly measure sufficient displacement data owing to the limitations of conventional displacement meters and the high cost of global navigation satellite systems (GNSS). Therefore, in this study, the previously developed estimation method was extended by combining displacement, slope, and strain to improve the estimation accuracy while reducing the need for high-cost GNSS. To validate the proposed model, the global deformation and internal forces of a cable-stayed bridge were estimated using limited multi-response data. The effect of multi-response data was analyzed, and the estimation performance of the extended method was verified by comparing its results with those of previous methods using a numerical model. The comparison results reveal that the extended method has better performance when estimating global responses than previous methods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.350-353
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• 1998

Recently, local governments have started projects of implementing Geographic Information Systems (GIS) as an infrastructure for the collection, storage, and analysis of the spatial information. So far, these projects have been approached from the areas such as the civil engineering and the computer science perspectives. Nonetheless, in order for a GIS project to be successful, it should be approached from the management perspectives as well. In this paper, the authors discusses about the management issues such as the determination of priority of the sub-projects, the organizational structure that takes charge of the project and the evaluation of the performance of the project.

• Steel and Composite Structures
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• v.33 no.3
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• pp.319-332
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• 2019

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.