• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Wind and Structures
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• v.37 no.3
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• pp.179-189
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• 2023

Multiple studies conducted in the past evaluated the conductor response under one tornado wind field, while the performance of transmission lines under different tornado wind fields still remains unknown. Thus, the objective of this paper is to estimate the variation in the conductor's critical longitudinal and transverse reactions under different tornado wind fields, as well as providing the corresponding critical tornado configurations. The considered full-scale tornadoes are the Spencer, South Dakota, 1998, the Stockton, Kansas, 2005 and the Goshen County, Wyoming, 2009. Computational Fluid Dynamics (CFD) simulations were previously conducted to develop these wind fields. All tornadoes have been rescaled to have a common velocity matching the upper limit of the F2 Fujita scale. Eight conductor systems, each including six spans, are considered in this paper. For each conductor, parametric studies are conducted by varying the location of the three tornado wind fields relative to the tower of interest, therefore the peak reactions associated with each tornado are determined. A semi-analytical closed-form solution, previously developed and validated, is used to calculate the reactions. The study conducted in this paper can be divided into two parts: In the first part, a parametric study considering a wide range of tornado locations is conducted. In the second part, the parametric study focuses on the tornado location leading to the critical tangential velocity on the tower. Based on this extensive parametric study, a critical tornado defined as the Design Tornado and its critical locations, tornado distance R = 125 m, tornado angle 𝜃 = 15° and 30°, are recommended for design purposes.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.192-194
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• 2015

The growing size and complex process in construction project recently leads to increase risk and the losses as well. Even though researchers have identified the major risk indicators, there is lack of comprehensive and quantitative research for identifying the relationship between the risk indicators and economic losses associated with construction projects. To address this shortage of research, this study defines risk indicators and create a framework to assess the influence of economic losses from the indicators. An insurance company's claim payout record was accepted as the dependent variable to reflect the real economic losses. Based on the claims, we categorized the causes and results of accidents. To establish framework, built environment vulnerability indicators and geographical vulnerability indicators were employed as the risk indicators. A Pearson correlation analysis was adopted to validate the relationship with loss ratio and risk indicators. Consequently, this framework and its results may offer significant references for under writers of insurance companies and loss prevention activities.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.32-45
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• 2023

Implementing Severe Accident Management (SAM) strategies is crucial for enhancing a nuclear power plant's resilience and safety against severe accidents conditions represented in the analysis of Station Blackout (SBO) event. Among these critical approaches, the In-Vessel Retention (IVR) through External Reactor Vessel Cooling (IVR-ERVC) strategy plays a key role in preventing vessel failure. This work is designed to evaluate the efficacy of the IVR strategy for a high-power density reactor APR1400. The APR1400's plant is represented and simulated under steady-state and transient conditions for a station blackout (SBO) accident scenario using the computer code, ASYST. The APR1400's thermal-hydraulic response is analyzed to assess its performance as it progresses toward a severe accident scenario during an extended SBO. The effectiveness of emergency operating procedures (EOPs) and severe accident management guidelines (SAMGs) are systematically examined to assess their ability to mitigate the accident. A group of associated key phenomena selected based on Phenomenon Identification and Ranking Tables (PIRT) and uncertain parameters are identified accordingly and then propagated within DAKOTA Uncertainty Quantification (UQ) framework until a statistically representative sample is obtained and hence determine the uncertainty bands of key system parameters. The Systems Engineering methodology is applied to direct the progression of work, ensuring systematic and efficient execution.

• Computers and Concrete
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• v.33 no.4
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• pp.471-479
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• 2024

In urban areas, appropriate backfilling design is necessary to prevent surface subsidence and subsurface cavities after excavation. Expandable foam grout (EFG), a mixture of cement, water, and an admixture, can be used for cavity filling because of its high flowability and volume expansion. EFG volume expansion induces a porous structure that can be quantified by the entrapped air content. This study observed the unit weight variations in the EFG before and after expansion depending on the various admixture-cement and water-cement ratios. Subsequently, the air content before and after expansion and the gravimetric expansion ratios were estimated from the measured unit weights. The air content before expansion linearly increased with an increase in the admixture-cement ratio, resulting in a decrease in the unit weight. The air content after the expansion and the expansion ratio increased nonlinearly, and the curves stabilized at a relatively high admixture-cement ratio. In particular, a reduced water-cement ratio limits the air content generation and expansion ratio, primarily because of the short setting time, even at a high admixture-cement ratio. Based on the results, the relationship between the maximum expansion ratio of EFG and the mixture ingredients (water-cement and admixture-cement ratios) was introduced.

• Food Science of Animal Resources
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• v.38 no.2
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• pp.362-375
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• 2018

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

• Korean Journal of Environmental Agriculture
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• v.28 no.4
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• pp.397-402
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• 2009

In Korea, salt stress is one of the major problems limiting crop production and eco-environmental quality in greenhouse soil. The objective of this study was to evaluate the effectiveness of organic residues (Chinese milk vetch, maize stalk, rice straw, and rye straw) for reducing salt activity in greenhouse soil. Organic residues was incorporated with salt-accumulated soil (EC, 3.0 dS $m^{-1}$) at the rate of 5% (wt $wt^{-1}$) and the changes of electrical conductivity (EC) was determined weekly for 8 weeks under incubation condition at $30^{\circ}C$. The EC, microbial biomass carbon (MBC), and water soluble ions in soil was strongly affected by C/N ratio of organic residues. After 8 weeks incubation, the concentration of water soluble $NO_3{^-},\;Ca^{2+}$, and $Mg^{2+}$ was significantly decreased in organic residues having high C/N ratio (maize stalk, rice straw, and rye straw) incorporated soil compared to organic residues having lower C/N ratio (Chinese milk vetch) incorporated soil. The EC value in Chinese milk vetch incorporated soil was higher than control treatment. In contrast, maize stalk, rice straw, and rye straw amended soil was highly decreased the EC value compared to control and Chinese milk vetch applied soil after 4 weeks incubation. Our results indicated that incorporation of organic residues having high C/N ratio (>30) could reduce salt activity resulting from reducing concentration of water soluble ions.