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

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.12 no.6
• /
• pp.241-248
• /
• 2008

The objective of sensitivity analyses is to identify critical variables of structural models and how their variability impacts mechanical response results. The sensitivity analyses have been used as significant basis data for practical applications of measuring and reinforcing fragile building structures. This study presents several sensitivity analysis methods for topological optimum designs of linear elastostatic structural systems. Numerical examples for structural analyses and topological optimum modeling demonstrate the reliability of sensitivities formulated in the present study.

• Smart Structures and Systems
• /
• v.33 no.4
• /
• pp.313-323
• /
• 2024

Deterioration of buildings is one of the biggest problems in modern society, and the importance of a safety diagnosis for old buildings is increasing. Therefore, most countries have legal maintenance and safety diagnosis regulations. However, the reliability of the existing safety diagnostic processes is reduced because they involve subjective judgments in the data collection. In addition, unstructured tasks increase rework rates, which are time-consuming and not cost-effective. Therefore, This paper proposed the method that can calculate the safety grade of deterioration automatically. For this, a DNN structure is generated by using existing precision inspection data and precision safety diagnostic data, and an objective building safety grade is calculated by applying status evaluation data obtained with a UAV, a laser scanner, and reverse engineering 3D models. This automated process is applied to 20 old buildings, taking about 40% less time than needed for a safety diagnosis from the existing manual operation based on the same building area. Subsequently, this study compares the resulting value for the safety grade with the already existing value to verify the accuracy of the grade calculation process, constructing the DNN with high accuracy at about 90%. This is expected to improve the reliability of aging buildings in the future, saving money and time compared to existing technologies, improving economic efficiency.

• Journal of Cadastre & Land InformatiX
• /
• v.49 no.1
• /
• pp.85-101
• /
• 2019

Recently, thank to the popularization of light-weight drone through the significant developments in computer technologies as well as the advanced automated procedures in photogrammetry, Unmanned Aircraft Systems have led to a growing interest in industry as a whole. Documentation, maintenance, and restoration projects of large scaled cultural property would required accurate 3D phenomenon modeling and efficient visual inspection methods. The object of this study verify on the accuracies achieved of 3D phenomenon reconstruction as well as on the validity of the preservation, maintenance and restoration of large scaled cultural property by UAS photogrammetry. The test object is cltural heritage(treasure 1324) that is the rock-carved standing Bodhisattva in Soraesan Mountain, Siheung, documented in Goryeo Period(918-1392). This standing Bodhisattva has of particular interests since it's size is largest stone Buddha carved in a rock wall and is wearing a lotus shaped crown that is decorated with arabesque patterns. The positioning accuracy of UAS photogrammetry were compared with non-target total station survey results on the check points after creating 3D phenomenal models in real world coordinates system from photos, and also the quantified informations documented by Culture Heritage Administration were compared with UAS on the bodhisattva image of thin lines. Especially, tests the validity of UAS photogrammetry as a alternative method of visual inspection methods. In particular, we examined the effectiveness of the two techniques as well as the relative fluctuation of rock surface for about 2 years through superposition analysis of 3D points cloud models produced by both UAS image analysis and ground laser scanning techniques. Comparison studies and experimental results prove the accuracy and efficient of UAS photogrammetry in 3D phenomenon modeling, maintenance and restoration for various large-sized Cultural Heritage.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.802-809
• /
• 2021

To cope with the increasing maintenance costs due to aging, the maintenance cost was evaluated from the perspective of asset management. The maintenance cost can be predicted based on the condition of the bridge, and the life cycle cost is used as an index. In general, the condition of a bridge has a wide distribution characteristic depending on the deterioration, load, and material characteristics. In this paper, to evaluate the effect of the bridge conditions on the life cycle cost, condition prediction models were constructed considering the service life, deterioration rate, and inspection error, which are the main variables of the bridge condition and life cycle cost calculation. In addition, condition prediction models were constructed based on the distribution of the health index to estimate the upper and lower bounds of the life cycle costs that can occur in individual bridges. Life cycle cost analysis showed that the life cycle cost differed significantly according to the condition of the bridge. Accordingly, research will be needed to increase the reliability of predicting the life cycle cost of individual bridges.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.20 no.1
• /
• pp.29-38
• /
• 1992

The primary objective of this study is to investigate factors and variables which have significant effects on user satisfaction with recreational facilities in Children's Grand Park in Pusan City, theregby to establish the developmental way of urban park and open space. To test the causal models of this research, the data were gathered by self-administered questionnaires from 1085 households in Pusan City which were selected by the multi-stage probability sampling method. The analysis of the data primarily consists of two phase : The fist analysis dealt exploratory factor analysis which identified major factors involved in satisfaction with recreational facilities in Children's Grand Park and the second analysis tested the fit of the causal models of this research by employing LISREL methodology. The factor analysis identified that five factors are involved in satisfaction with recreational facilities. The five factors of satisfaction with recreational facilities are convenience and maintanance facilities, learnded recreational facilities, spaces for repose and relaxation, spaces for active recreation failities, and facilities for health and physical facilities. The second phase analysis tested the fit of the causal models for satisfaction with recreational facilities to the data and identified statistically significant causal linkage among overall satisfaction with Children's Grand Park, other endogenous factors and exogenous variables. Overall fits of both causal models were very good. Among endogenous factors, facilities for repose and relaxation, facilities for convenience and maintenance, learnded recreational facilities were identified as having significant effects on overall satisfaction. Exogenous variables which have significant effects on endogenous variables were also identified. These significant relationships indicate important factors and variables that should be considered in planning and development of urban park and open space. On the basis of these significant causal relationships, way for delovepment of urban park and open space were suggested.

• The KIPS Transactions:PartD
• /
• v.11D no.4
• /
• pp.975-982
• /
• 2004

In current P2P concept, pure P2P and Hybrid P2P structures are used commonly. Gnutella and Ktella are forms of pure P2P. and forms of Hybrid P2P are innumerable. File searching models exist in these models. These models provide group management for file sharing, searching and indexing. The general file sharing model is good at maintaining connectivity. However, it is defective in group management. Therefore, this study approaches hierarchical structure in file sharing models through routing technique and backup system. This system was designed so that the user was able to maintain group efficiency and connection reliability in large-scale network.

• International Journal of Concrete Structures and Materials
• /
• v.11 no.2
• /
• pp.199-213
• /
• 2017

Chloride penetration is among the main causes of corrosion initiation in reinforced concrete (RC) structures producing premature degradations. Weather and exposure conditions directly affect chloride ingress mechanisms and therefore the operational service life and safety of RC structures. Consequently, comprehensive chloride ingress models are useful tools to estimate corrosion initiation risks and minimize maintenance costs for RC structures placed under chloride-contaminated environments. This paper first presents a coupled thermo-hydro-chemical model for predicting chloride penetration into concrete that accounts for realistic weather conditions. This complete numerical model takes into account multiple factors affecting chloride ingress such as diffusion, convection, chloride binding, ionic interaction, and concrete aging. Since the complete model could be computationally expensive for long-term assessment, this study also proposes model simplifications in order to reduce the computational cost. Long-term chloride assessments of complete and reduced models are compared for three locations in France (Brest, Strasbourg and Nice) characterized by different weather and exposure conditions (tidal zone, de-icing salts and salt spray). The comparative study indicates that the reduced model is computationally efficient and accurate for long-term chloride ingress modeling in comparison to the complete one. Given that long-term assessment requires larger climate databases, this research also studies how climate models may affect chloride ingress assessment. The results indicate that the selection of climate models as well as the considered training periods introduce significant errors for mid- and long- term chloride ingress assessment.

• Journal of the Korea Safety Management & Science
• /
• v.16 no.2
• /
• pp.217-230
• /
• 2014

The purpose of this study is to develop the Generalized Depreciation Function (GDF) and Winfrey Depreciation Function (WDF) by reviewing methods for the depreciation accountings. The Depreciation Accounting Models (DAM), including straight-line model, declining-balance model, sum-of-the-year-digit model and sinking fund model presented in this paper, are reclassified into the charging pattern of increasing type, decreasing type and constant type. This paper also discusses the development of the GDFs based on convex type, concave type and constant type according to the demand pattern of product, frequency of plant usage, deterioration of time, relative inadequacy, Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) of the Total Productive Maintenance (TPM). The WDFs presented in this paper depict a sudden degradation of plant performance by measuring the change of TPM activity at the midpoint of useful life of asset. The WDFs are classified into left-modal type, symmetrical type and right-modal type by varying the value of skewness and kurtosis. Moreover, three increasing patterns, such as convex, concave and linear types, are used in this paper to present the distinct identification of WFDs by using Instantaneous Depreciation Rate (IDR) in terms of Performance Depreciation Function (PDF) and Depreciation Density Function (DDF). In order to have better understanding of depreciation models, the numerical examples are used for evaluating the Net Operating Less Adjusted Tax (NOPLAT) and Economic Value Added (EVA). It is concluded that the depreciation models showing a large dispersion of EVA require the adjustment of NOPLAT and Invested Capital (IC) based on the objective cash basis and net operating activity for reducing the variation of EVA.

• Journal of Information Processing Systems
• /
• v.13 no.4
• /
• pp.778-804
• /
• 2017

Software today has become an inseparable part of our life. In order to achieve the ever demanding needs of customers, it has to rapidly evolve and include a number of changes. In this paper, our aim is to study the relationship of object oriented metrics with change proneness attribute of a class. Prediction models based on this study can help us in identifying change prone classes of a software. We can then focus our efforts on these change prone classes during testing to yield a better quality software. Previously, researchers have used statistical methods for predicting change prone classes. But machine learning methods are rarely used for identification of change prone classes. In our study, we evaluate and compare the performances of ten machine learning methods with the statistical method. This evaluation is based on two open source software systems developed in Java language. We also validated the developed prediction models using other software data set in the same domain (3D modelling). The performance of the predicted models was evaluated using receiver operating characteristic analysis. The results indicate that the machine learning methods are at par with the statistical method for prediction of change prone classes. Another analysis showed that the models constructed for a software can also be used to predict change prone nature of classes of another software in the same domain. This study would help developers in performing effective regression testing at low cost and effort. It will also help the developers to design an effective model that results in less change prone classes, hence better maintenance.