• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.351-368
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• 2021

This paper, by applying a reliability-based framework, develops seismic vulnerability macrozonation maps for Tehran, the capital and one of the most earthquake-vulnerable city of Iran. Seismic performance assessment of 3-, 4- and 5-story steel moment resisting frames (SMRFs), designed according to ASCE/SEI 41-17 and Iranian Code of Practice for Seismic Resistant Design of Buildings (2800 Standard), is investigated in terms of overall maximum inter-story drift ratio (MIDR) and unit repair cost ratio which is hereafter known as "damage ratio". To this end, Tehran city is first meshed into a network of 66 points to numerically locate low- to mid-rise SMRFs. Active faults around Tehran are next modeled explicitly. Two different combination of faults, based on available seismological data, are then developed to explore the impact of choosing a proper seismic scenario. In addition, soil effect is exclusively addressed. After building analytical models, reliability methods in combination with structure-specific probabilistic models are applied to predict demand and damage ratio of structures in a cost-effective paradigm. Due to capability of proposed methodology incorporating both aleatory and epistemic uncertainties explicitly, this framework which is centered on the regional demand and damage ratio estimation via structure-specific characteristics can efficiently pave the way for decision makers to find the most vulnerable area in a regional scale. This technical basis can also be adapted to any other structures which the demand and/or damage ratio prediction models are developed.

• Structural Engineering and Mechanics
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• v.87 no.6
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• pp.555-574
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• 2023

An efficient optimization algorithm and damage-sensitive objective function are two main components in optimization-based Finite Element Model Updating (FEMU). A suitable combination of these components can considerably affect damage detection accuracy. In this study, a new hybrid damage-sensitive objective function is proposed based on combining two different objection functions to detect the location and extent of damage in structures. The first one is based on Generalized Pseudo Modal Strain Energy (GPMSE), and the second is based on the element's Generalized Flexibility Matrix (GFM). Four well-known population-based metaheuristic algorithms are used to solve the problem and report the optimal solution as damage detection results. These algorithms consist of Cuckoo Search (CS), Teaching-Learning-Based Optimization (TLBO), Moth Flame Optimization (MFO), and Jaya. Three numerical examples and one experimental study are studied to illustrate the capability of the proposed method. The performance of the considered metaheuristics is also compared with each other to choose the most suitable optimizer in structural damage detection. The numerical examinations on truss and frame structures with considering the effects of measurement noise and availability of only the first few vibrating modes reveal the good performance of the proposed technique in identifying damage locations and their severities. Experimental examinations on a six-story shear building structure tested on a shake table also indicate that this method can be considered as a suitable technique for damage assessment of shear building structures.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.403-406
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• 2005

In order to evaluate the Capability of ETM+ remotely- sensed data to provide 'Forest-shrub land-Rangeland' cover type map in areas near the timberline of northern forests of Iran, the data were analyzed in a portion of nearly 790 ha located in Neka-Zalemroud region. First, ortho-rectification process was used to correct the geometric errors of the image, yielding 0/68 and 0/69 pixels of RMS. error in X and Y axis, respectively. The original and panchromatic bands were fused using PANSHARP Statistical module. The ground truth map was made using 1 ha field plots in a systematic-random sampling grid, and vegetative form of trees, shrubs and rangelands was recorded as a criteria to name the plots. A set of channels including original bands, NDVI and IR/R indices and first components of PCI from visible and infrared bands, was used for classification procedure. Pair-wise divergence through CHNSEL command was used, In order to evaluate the separability of classes and selection of optimal channels. Classification was performed using ML classifier, on both original and fused data sets. Showing the best results of $67\%$ of overall accuracy, and 0/43 of Kappa coefficient in original data set. Due to the results represented above, it's concluded that ETM+ data has an intermediate capability to fulfill the spectral variations of three form- based classes over the study area.

• Smart Structures and Systems
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• v.5 no.4
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• pp.345-355
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• 2009

This article describes the development of a fiber optic accelerometer based on Fiber Bragg Gratings (FBG). The accelerometer utilizes the stiffness of the optical fiber and a lumped mass in the design. Acceleration is measured by the FBG in response to the vibration of the fiber optic mass system. The wavelength shift of FBG is proportional to the change in acceleration, and the gauge factor pertains to the shift in wavelength as a function of acceleration. Low frequency version of the accelerometer was developed for applications in monitoring bridges. The accelerometer was first evaluated in laboratory settings and then employed in a demonstration project for condition assessment of a bridge. Laboratory experiments involved evaluation of the sensitivity and resolution of measurements under a series of low frequency low amplitude conditions. The main feature of this accelerometer is single channel multiplexing capability rendering the system highly practical for application in condition assessment of bridges. This feature of the accelerometer was evaluated by using the system during ambient vibration tests of a bridge. The Frequency Domain Decomposition method was employed to identify the mode shapes and natural frequencies of the bridge. Results were compared with the data acquired from the conventional accelerometers.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.692-701
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• 2019

Recently, instrumentation and control (I&C) systems in nuclear power plants have undergone digitalization. Owing to the unique characteristics of digital I&C systems, the reliability analysis of digital systems has become an important element of probabilistic safety assessment (PSA). In a reliability analysis of digital systems, fault-tolerant techniques and their effectiveness must be considered. A fault injection experiment was performed on a safety-critical digital I&C system developed for nuclear power plants to evaluate the effectiveness of fault-tolerant techniques implemented in the target system. A software-implemented fault injection in which faults were injected into the memory area was used based on the assumption that all faults in the target system will be reflected in the faults in the memory. To reduce the number of required fault injection experiments, the memory assigned to the target software was analyzed. In addition, to observe the effect of the fault detection coverage of fault-tolerant techniques, a PSA model was developed. The analysis of the experimental result also can be used to identify weak points of fault-tolerant techniques for capability improvement of fault-tolerant techniques

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1090-1100
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• 2024

While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.866-872
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• 2024

Design Extension Conditions (DEC)-A assessments of the operating nuclear power plants are generally considered for the purpose of getting additional safety demonstrations of their capability to undergo conditions that are generally more severe than DBAs by features implemented in the design and accident management measures. The pursued methodology is generally based upon Best Estimate approaches aiming at verifying that the safety limits in terms of integrity of the barriers against eventual large or early releases of radioactive material are fulfilled. These aspects are nowadays being experimentally and analytically addressed within the OECD/NEA experimental projects like the ATLAS and PKL series where a set of DEC-A experiments are considered. In this paper, experiments related to SBLOCA at the vessel upper head of the pressurized vessel of ATLAS and PKL are analytically assessed using the CATHARE code. These experiments includes issues related to common cause failure of the safety injection system and operator actions for preventing core excessive overheating. It is shown that, on the one hand, the safety features embedded in the design together with the operator actions are capable to prevent the progression towards a severe accident state and on the other hand, the code prediction capabilities for such scenario are generally good but still to be enhanced.

• Journal of Korea Water Resources Association
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• v.48 no.8
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• pp.647-657
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• 2015

This study assessed the water use vulnerability for 12 basins of South Korea. The annual runoff of 12 basins are derived using a Soil and Water Assessment Tool (SWAT) and the calculated runoff per unit area and population are compared with each basin. The 18 indicators are selected in order to assess the vulnerability. Those are classified by aspects of demand, loss and supply of water use. Their weighting values used Entropy method to determine objective weights. To quantitatively assess the water use vulnerability, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on multi-criteria decision making are applied. The results show that the water availability vulnerability of Hyeongsan River has the highest value followed by Sapgyo River; Dongjin River; Seomjin River; Anseong River; Mangyung River; Nakdong River; Tamjin River; Youngsan River, Geum River; Taehwa River; and Han River. The result of this study has a capability to provide references for the index deveopment of climate change vulnerability assessment.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.243-253
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• 2013

As global competition is more intensive, SCM is becoming more and more important in order to achieve competitiveness. It is necessary for firms to understand weak points among the integrated supply chain through supply chain assessment in order to implement SCM successfully and cost-effectively. In this paper, we review previous studies of SCM performance measurements and propose a supply chain assessment framework for shipbuilding industry, which is one of the major ETO industry. Based on 3 supply chain domains which are demand, supply, and product management, we decompose the whole supply chain process into 3 level lower processes and propose capability measurement matric for shipbuilding firms. A case study has been conducted to validate the proposed framework and results are presented at the end.