• 한국터널지하공간학회 논문집
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• 제18권2호
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• pp.119-128
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• 2016

성능기반 설계에서 구조물의 안정성은 손상 상태와 이를 수치화한 손상 지수에 의해 평가한다. 지상 구조물에 대해서는 이들이 비교적 명확하게 정의되어 있으나 지중 구조물에 대한 연구 수행 사례는 매우 제한적이다. 본 연구에서는 국내 지하철 시스템에 널리 사용되는 박스형 개착식 터널에 작용하는 지진하중에 의한 손상 상태와 손상 지수를 일련의 비탄성 프레임 해석을 통하여 규명하였다. 터널의 3 단계 손상 상태는 구조물에 발생한 소성 힌지의 수에 의해 정의하였다. 손상 지수는 터널 구조 부재의 탄성 모멘트와 항복 모멘트의 비로 정의하여 탄성 해석만으로도 비탄성 거동과 파괴 메커니즘의 모사가 가능하도록 하였다. 또한 손상 지수를 자유장 전단 변형률의 함수로도 제시하였다. 전단 변형률은 1 차원 지반응답해석으로 쉽게 계산할 수 있으므로 이를 이용하여 간편하게 박스형 터널의 초기 내진 안정성 평가가 가능할 것으로 판단된다. 보다 일반적이고 보편적인 적용성 확보를 위해서는 추후 포괄적인 해석을 수행하여 다양한 형태의 터널과 지반에서의 전단 변형률 분포와 불확실성에 대한 연구가 진행되어야 할 것이다. 본 연구에서 제시된 터널 내진설계를 위한 손상 상태, 손상 지수, 그리고 전단파 속도 및 전단변형률 간의 상호관계 플래트폼은 새로운 아이디어를 담고 있으며 추후 설계에 널리 활용될 수 있을 것으로 판단된다.

• 한국도로학회논문집
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• 제19권5호
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• pp.1-11
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• 2017

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS : Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS:The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.

• 한국구조물진단유지관리공학회 논문집
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• 제6권4호
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• pp.189-199
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• 2002

This study is intended to propose a systematic procedure for the development of the conditional assessment based on the safety of structures and the cost effective performance criteria for designing and upgrading of bridge structures. As a result, a set of cost function models for a life cycle cost analysis of bridge structures is proposed and thus the expected total life cycle costs (ETLCC) including initial (design, testing and construction) costs and direct/indirect damage costs considering repair and replacement costs, human losses and property damage costs, road user costs, and indirect regional economic losses costs. Also, the optimum safety indices are presented based on the expected total cost minimization function using only three parameters of the failure cost to the initial cost (${\tau}$), the extent of increased initial cost by improvement of safety (${\nu}$) and the order of an initial cost function (n). Through the enough numerical invetigations, we can positively conclude that the proposed optimum design procedure for bridge structures based on the ETLCC will lead to more rational, economical and safer design.

• Structural Engineering and Mechanics
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• 제83권4호
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• 산업공학
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• 제15권4호
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• pp.460-473
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• 2002

The objective of this paper is to design a Goal Alternatives Disposition System having three main subsystems for setting, evaluating and selecting goal alternatives. For setting of goal alternatives, System Alternatives Tree(SAT) structure will be developed, which has a computation algorithm for setting decision alternatives by the concept of System Priority Number(SPN). For evaluating and selecting of goal alternatives; First, Normative and Exploratory Priority Indices which consider technical performance to the goal, cost and feasibility are developed respectively. Second, Integrated Priority Index is built up to determine the total priority of the Goal Alternatives Disposition(GAD) system. For the design and verification of the GAD system, technological forecasting structure theory, systems engineering methodology will be used.

• Structural Engineering and Mechanics
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• 제32권1호
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• pp.127-145
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• 2009

A novel approach is proposed to effectively estimate the quantile functions of normalized performance indices of reliability constraints in a reliability-based optimization (RBO) problem. These quantile functions are not only estimated as functions of exceedance probabilities but also as functions of the design variables of the target RBO problem. Once these quantile functions are obtained, all reliability constraints in the target RBO problem can be transformed into non-probabilistic ordinary ones, and the RBO problem can be solved as if it is an ordinary optimization problem. Two numerical examples are investigated to verify the proposed novel approach. The results show that the approach may be capable of finding approximate solutions that are close to the actual solution of the target RBO problem.

• Structural Engineering and Mechanics
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• 제61권2호
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• pp.171-181
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• 2017

This paper summarizes the study on effect of ceramic waste powder as partial substitute to cement in binary blend and along with silica fume in ternary blend high strength concrete in normal and aggressive environments. Strength parameters such as compression & tension and durability indices such as corrosion measurement, deterioration, water absorption and porosity were studied. Ceramic waste powder was used in three different percentages namely 5, 10 and 15 with constant percentage of silica fume (1%) as substitutes to cement in ternary blend high strength concrete was investigated. After a detailed investigation, it was understood that concrete with 15% ceramic waste powder registered maximum performance. Increase of ceramic waste powder offered better resistance to deterioration of concrete.

• Smart Structures and Systems
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• 제16권2호
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• pp.243-262
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• 2015

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2000년도 추계학술대회 논문집
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• pp.79-83
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• 2000

A sliding mode fuzzy control (SMFC) with disturbance estimator is applied to design a controller for the third generation benchmark problem on an wind-excited building. A distinctive feature in vibration control of large civil infrastructure is the existence of large disturbances, such as wind, earthquake, and sea wave forces. Those disturbances govern the behavior of the structure, however, they cannot be precisely measured, especially for the case of wind-induced vibration control. Since the structural accelerations are measured only at a limited number of locations without the measurement of the wind forces, the structure of the conventional control may have the feed-back loop only. General structure of the SMFC is composed of a compensation part and a convergent part. The compensation part prevents the system diverge, and the convergent part makes the system converge to the sliding surface. The compensation part uses not only the structural response measurement but also the disturbance measurement, so the SMFC has a feed-back loop and a feed-forward loop. To realize the virtual feed-forward loop for the wind-induced vibration control, disturbance estimation filter is introduced. the structure of the filter is constructed based on an auto regressive model for the stochastic wind force. This filter estimates the wind force at each time instance based on the measured structural responses and the stochastic information of the wind force. For the verification of the proposed algorithm, a numerical simulation is carried out on the benchmark problem of a wind-excited building. The results indicate that the present control algorithm is very efficient for reducing the wind-induced vibration and that the performance indices improve as the filter for wind force estimation is employed.

• Smart Structures and Systems
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• 제30권6호
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.