• Environmental Engineering Research
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• v.21 no.3
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• pp.256-264
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• 2016

2-Methylisoborneol (2-MIB) is one of typical odorants in potable water sources, which is hardly removed by conventional water treatment process. In this study, three strains capable of removing 2-MIB singly from drinking water were isolated from activated carbon of sand filter. They were identified to be Shinella zoogloeoides, Bacillus idriensis and Chitinophagaceae bacterium based on 16S rRNA gene sequence analysis. In mineral salts medium without external carbon source, removal efficiencies of $20{\mu}g/L$ 2-MIB in three days were 23.3%, 32.9% and 17.0% for Shinella zoogloeoides, Bacillus idriensis and Chitinophagaceae bacterium, respectively. The biodegradation of 2-MIB was significantly improved with the presence of cometabolism carbon(glycerol, glucose, etc.). In the period of 20 days, Bacillus idriensis can remove 2 mg/L MIB to $368.2{\mu}g/L$ and $315.4{\mu}g/L$ in mineral salts medium without and with glycerol respectively. The removal of 2-MIB by Bacillus idriensis was from 2 mg/L to $958.4{\mu}g/L$ in Xiba river samples on 15 days.

• Membrane and Water Treatment
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• v.8 no.2
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• pp.201-210
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• 2017

We investigated the effect of applied voltage and electrolyte concentration on the nitrate removal and its energy/current efficiency during the electrodialysis. The current increased as the applied voltage increased up to 30 V showing the limiting current density around 20 V. The nitrate removal efficiency (31 to 71% in 240 min) and energy consumption (11 to $77W{\cdot}h/L$) gradually increased as the applied voltage increased from 10 to 30 V. The highest current efficiency was obtained at 20 V. The increase in electrolyte concentration from 100 to 500 mM led to the dramatic increase of nitrate removal efficiency with much faster removal kinetics (100 % in 10 min).

• Journal of Microbiology and Biotechnology
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• v.23 no.10
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• pp.1472-1477
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• 2013

Microalgae have been regarded as a natural resource for sustainable materials and fuels, as well as for removal of nutrients and micropollutants from wastewater, and their interaction with bacteria in wastewater is a critical factor to consider because of the microbial diversity and complexity in a variety of wastewater conditions. Despite their importance, very little is known about the ecological interactions between algae and bacteria in a wastewater environment. In this study, we characterized the wastewater bacterial community in response to the growth of a Selenastrum gracile UTEX 325 population in a real municipal wastewater environment. The Roche 454 GS-FLX Titanium pyrosequencing technique was used for indepth analysis of amplicons of 16S rRNA genes from different conditions in each reactor, with and without the algal population. The algal growth reduced the bacterial diversity and affected the bacterial community structure in the wastewater. The following in-depth analysis of the deep-sequenced amplicons showed that the algal growth selectively stimulated Sphingobacteria class members, especially the Sediminibacterium genus population, in the municipal wastewater environment.

• Smart Structures and Systems
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• v.17 no.4
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• pp.669-690
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• 2016

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.

• Smart Structures and Systems
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• v.29 no.1
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• Computers and Concrete
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• v.31 no.3
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• pp.223-239
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• 2023

Geometric nonlinear performance simulation and analysis of complex modern buildings and industrial products require high-performance shell elements. Balancing multiple aspects of performance in the one geometric nonlinear analysis element remains challenging. We present a new shell element, flat shell DKMGQ-CR (Co-rotational Discrete Kirchhoff-Mindlin Generalized Conforming Quadrilateral), for linear and geometric nonlinear analysis of both thick and thin shells. The DKMGQ-CR shell element was developed by combining the advantages of high-performance membrane and plate elements in a unified coordinate system and introducing the co-rotational formulation to adapt to large deformation analysis. The effectiveness of linear and geometric nonlinear analysis by DKMGQ-CR is verified through the tests of several classical numerical benchmarks. The computational results show that the proposed new element adapts to mesh distortion and effectively alleviates shear and membrane locking problems in linear and geometric nonlinear analysis. Furthermore, the DKMGQ-CR demonstrates high performance in analyzing thick and thin shells. The proposed element DKMGQ-CR is expected to provide an accurate, efficient, and convenient tool for the geometric nonlinear analysis of shells.

• Computers and Concrete
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• v.33 no.5
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• pp.595-603
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• 2024

In the construction industry, there has been a surge in the implementation of high-tech equipment in recent years. Various technologies are being considered as potential solutions for future construction projects. Building information modeling (BIM), which utilizes advanced equipment, is a promising solution among these technologies. The need for safety inspection has also increased with the aging structures. Nevertheless, traditional safety inspection technology falls short of meeting this demand as it heavily relies on the subjective opinions of workers. This inadequacy highlights the need for advancements in existing maintenance technology. Research on building safety inspection using 3D laser scanners has notably increased. Laser scanners that use light detection and ranging (LiDAR) can quickly and accurately acquire producing information, which can be realized through reverse engineering by modeling point cloud data. This study introduces an innovative evaluation system for building safety using a 3D laser scanner. The system was used to assess the safety of an existing three-story building by implementing a reverse engineering technique. The 3D digital data are obtained from the scanner to detect defects and deflections in and outside the building and to create an as-built BIM. Subsequently, the as-built structural model of the building was generated using the reverse engineering approach and used for structural analysis. The acquired information, including deformations and dimensions, is compared with the expected values to evaluate the effectiveness of the proposed technique.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.55.1-55.1
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• 2009

• Environmental Engineering Research
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• v.9 no.6
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• pp.288-298
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• 2004

• Environmental Engineering Research
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• v.16 no.1
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• pp.19-27
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• 2011

This paper introduces an approach to identify the total energy consumption with subsequent $CO_2$ emissions, for both industrial and non-industrial sectors. Statistical data for 2005 were compiled in a national account system to construct an energy input-output table for investigating the influence between energy demand and supply activities. The methodological approach was applied to South Korea. Twelve types of energy and fifteen industrial and non-industrial sectors are formed as the compartments of the input-output table. The results provided quantitative details of the energy consumption and identified the significant contributions from each sector. An impact analysis on the $CO_2$ emissions for the demand side was also conducted for comparison with the supply side.