Effect of Degree of Weathering on Dynamic Properties of Weathered Granite Soils

The strength-deformation characteristics of weathered granite soils are determined by the characteristic of bedrock before the weathering and weathering environment. Generally, the degree of weathering is changes with depth and affects the dynamic properties of soil. But there are very few studies of the effect of degree of weathering on dynamic properties of soils in Korea. In this study, dynamic tests were performed with resonant column after preparing the specimens which were remolded with disturbed soils, and the effect of degree of weathering on dynamic properties, such as shear modulus, damping ratio is considered. In addition, the relationships among shear strain, normalized shear modulus(G/Gmax) and normalized damping ratio(D/Dmin) are compared with other studies

Decentralized system identification using stochastic subspace identification for wireless sensor networks

Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model. ??? 2015 by the authors; licensee MDPI, Basel, Switzerlandopen0

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles.

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera itself moving; thus, the displacements of the structure obtained by processing UAV video are relative to the UAV camera. Some efforts have been reported to compensate for the camera motion, but they require certain assumptions that may be difficult to satisfy. This paper proposes a new method for structural system identification using the UAV video directly. Several challenges are addressed, including: (1) estimation of an appropriate scale factor; and (2) compensation for the rolling shutter effect. Experimental validation is carried out to validate the proposed approach. The experimental results demonstrate the efficacy and significant potential of the proposed approach

Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion

Displacement responses of a bridge as a result of external loadings provide crucial information regarding structural integrity and current conditions. Due to the relative characteristic of displacement, the conventional measurement approach requires reference points to firmly install the transducers, while the points are often unavailable for bridges. In this paper, a displacement estimation approach using Kalman filter-based data fusion is proposed to provide a practical means for displacement measurement. The proposed method enables accurate displacement estimation by optimally utilizing acceleration and strain in combination that have high availability and are free from reference points for sensor installation. The Kalman filter is formulated using a state-space model representing the double integration of acceleration and model-based strain-displacement relationship. The validation of the proposed method is conducted successfully by a numerical simulation and a field experiment, which shows the efficacy and accuracy of the proposed approach in bridge displacement measurement.ope

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Kalman Filter-based Data Recovery in Wireless Smart Sensor Network for Infrastructure Monitoring

Extensive research effort has been made during the last decade to utilize wireless smart sensors for evaluating and monitoring structural integrity of civil engineering structures. The wireless smart sensor commonly has sensing and embedded computation capabilities as well as wireless communication that provide strong potential to overcome shortcomings of traditional wired sensor systems such as high equipment and installation cost. However, sensor malfunctioning particularly in case of long-term monitoring and unreliable wireless communication in harsh environment are the critical issues that should be properly tackled for a wider adoption of wireless smart sensors in practice. This study presents a wireless smart sensor network(WSSN) that can estimate unmeasured responses for the purpose of data recovery at unresponsive sensor nodes. A software program that runs on WSSN is developed to estimate the unmeasured responses from the measured using the Kalman filter. The performance of the developed network software is experimentally verified by estimating unmeasured acceleration responses using a simply-supported beam.clos

PutidaNET: Interactome database service and network analysis of Pseudomonas putida KT2440

<p>Abstract</p> <p>Background</p> <p><it>Pseudomonas putida </it>KT2440 (<it>P. putida </it>KT2440) is a highly versatile saprophytic soil bacterium. It is a certified bio-safety host for transferring foreign genes. Therefore, the bacterium is used as a model organism for genetic and physiological studies and for the development of biotechnological applications. In order to provide a more systematic application of the organism, we have constructed a protein-protein interaction (PPI) network analysis system of <it>P. putida </it>KT2440.</p> <p>Results</p> <p>PutidaNET is a comprehensive interaction database and server of <it>P. putida </it>KT2440 which is generated from three protein-protein interaction (PPI) methods. We used PSIMAP (Protein Structural Interactome MAP), PEIMAP (Protein Experimental Interactome MAP), and Domain-domain interactions using iPfam. PutidaNET contains 3,254 proteins, and 82,019 possible interactions consisting of 61,011 (PSIMAP), 4,293 (PEIMAP), and 30,043 (iPfam) interaction pairs except for self interaction. Also, we performed a case study by integrating a protein interaction network and experimental 1-DE/MS-MS analysis data <it>P. putida</it>. We found that 1) major functional modules are involved in various metabolic pathways and ribosomes, and 2) existing PPI sub-networks that are specific to succinate or benzoate metabolism are not in the center as predicted.</p> <p>Conclusion</p> <p>We introduce the PutidaNET which provides predicted interaction partners and functional analyses such as physicochemical properties, KEGG pathway assignment, and Gene Ontology mapping of <it>P. putida </it>KT2440 PutidaNET is freely available at <url>http://sequenceome.kobic.kr/PutidaNET</url>.</p