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Structural health monitoring (SHM) is a technique to diagnose an accurate and reliable condition of civil infrastructure by collecting and analyzing responses from distributed sensors. In recent years, aging civil structures have been increasing and they require further developed SHM technology for development of sustainable society. Wireless smart sensor and network technology, which is one of the recently emerging SHM techniques, enables more effective and economic SHM system in comparison to the existing wired systems. Researchers continue on development of the capability and extension of wireless smart sensors, and implement performance validation in various in-laboratory and outdoor full-scale experiments. This paper presents a summary of recent (mostly after 2010) researches on smart sensors, focused on the newly developed hardware, software, and validation examples of the developed smart sensors.ope

Particulate counter electrode system for enhanced light harvesting in dye-sensitized solar cells

A particulate counter electrode with photo scattering and redox catalytic properties is applied to dye sensitized solar cells (DSSCs) in order to improve photo conversion efficiency and simplify the assembly process. Our particulate counter electrode acts as both a photo reflecting layer and a catalyst for reduction of electrolyte. The reflective and catalytic properties of the electrode are investigated through optical and electrochemical analysis, respectively. A short circuit current density enhancement is observed in the DSSCs without the need to add an additional reflecting layer to the electrode. This leads to a simplified assembly process. (C) 2013 Optical Society of Americ

Principles and applications of ultrasonic-based nondestructive methods for self-healing in cementitious materials

Recently, self-healing technologies have emerged as a promising approach to extend the service life of social infrastructure in the field of concrete construction. However, current evaluations of the self-healing technologies developed for cementitious materials are mostly limited to lab-scale experiments to inspect changes in surface crack width (by optical microscopy) and permeability. Furthermore, there is a universal lack of unified test methods to assess the effectiveness of self-healing technologies. Particularly, with respect to the self-healing of concrete applied in actual construction, nondestructive test methods are required to avoid interrupting the use of the structures under evaluation. This paper presents a review of all existing research on the principles of ultrasonic test methods and case studies pertaining to self-healing concrete. The main objective of the study is to examine the applicability and limitation of various ultrasonic test methods in assessing the self-healing performance. Finally, future directions on the development of reliable assessment methods for self-healing cementitious materials are suggested.ope

Electrochemical Investigation of High-Performance Dye-Sensitized Solar Cells Based on Molybdenum for Preparation of Counter Electrode

In order to improve the photocurrent conversion efficiency of dye-sensitized solar cells (DSSCs), we studied an alternative conductor for the counter electrode and focused on molybdenum (Mo) instead of conventional fluorine-doped tin oxide (FTO). Because Mo has a similar work function to FTO for band alignment, better formability of platinum (Pt), and a low electric resistance, using a counter electrode made of Mo instead of FTO lead to the enhancement of the catalytic reaction of the redox couple, reduce the interior resistance of the DSSCs, and prevent energy-barrier formation. Using electrical measurements under a 1-sun condition (100 mW/cm(2), AM 1.5), we determined that the fill factor (FF) and photocurrent conversion efficiency (eta) of DSSCs with a Mo electrode were respectively improved by 7.75% and 5.59% with respect to those of DSSCs with an FTO electrode. Moreover, we have investigated the origin of the improved performance through surface morphology analyses such as scanning electron microscopy and electrochemical analyses including cyclic voltammetry and impedance spectroscopy

Applicability of Diffuse Ultrasound to Evaluation of the Water Permeability and Chloride Ion Penetrability of Cracked Concrete

This study aims to explore the applicability of diffuse ultrasound to the evaluation of water permeability and chloride ion penetrability of cracked concrete. Lab-scale experiments were conducted on disk-shaped concrete specimens, each having a different width of a penetrating crack that was generated by splitting tension along the centerline. The average crack width of each specimen was determined using an image binarization technique. The diffuse ultrasound test employed signals in the frequency range of 200 to 440 kHz. The water flow rate was measured using a constant water-head permeability method, and the chloride diffusion coefficient was determined using a modified steady-state migration method. Then, the effects of crack width on the diffusion characteristics of ultrasound (i.e., diffusivity, dissipation), water flow rate, and chloride diffusion coefficient are investigated. The correlations between the water flow rate and diffuse ultrasound parameters, and between the chloride diffusion coefficient and diffuse ultrasound parameters, are examined. The results suggest that diffuse ultrasound is a promising method for assessing the water permeability and chloride ion penetrability of cracked concrete

Surface-Wave Based Model for Estimation of Discontinuity Depth in Concrete

In this paper, we propose an accurate and practical model for the estimation of surface-breaking discontinuity (i.e., crack) depth in concrete through quantitative characterization of surface-wave transmission across the discontinuity. The effects of three different mixture types (mortar, normal strength concrete, and high strength concrete) and four different simulated crack depths on surface-wave transmission were examined through experiments carried out on lab-scale concrete specimens. The crack depth estimation model is based on a surface-wave spectral energy approach that is capable of taking into account a wide range of wave frequencies. The accuracy of the proposed crack depth estimation model is validated by root mean square error analysis of data from repeated spectral energy transmission ratio measurements for each specimen

Effects of Lowering Dialysate Calcium Concentrations on Arterial Stiffness in Patients Undergoing Hemodialysis

BACKGROUND/AIMS: We assessed changes in hemodynamic and arterial stiffness parameters following reductions of dialysate calcium concentrations in patients undergoing hemodialysis. METHODS: In this prospective study, 20 patients on maintenance hemodialysis (10 females, 10 males) with dialysate calcium concentrations of 1.75 mmol/L were enrolled. At the start of the study, the dialysate calcium level was lowered to 1.50 mmol/L. Serial changes in biochemical, hemodynamic, and arterial stiffness parameters, including pulse wave velocity (PWV) and augmentation index (AIx), were assessed every 2 months for 6 months. We also examined changes in the calcification-inhibitory protein, serum fetuin-A. RESULTS: During the 6-month study period, serum total calcium and ionized calcium decreased consistently (9.5 ± 1.0 to 9.0 ± 0.7, p = 0.002 vs. 1.3 ± 0.1 to 1.1 ± 0.1, p = 0.035). Although no apparent changes in blood pressure were observed, heart-femoral PWW (hf-PWV) and AIx showed significant improvement (p = 0.012, 0.043, respectively). Repeated-measures ANOVA indicated a significant effect of lowering dialysate calcium on hf-PWV (F = 4.58, p = 0.004) and AIx (F = 2.55, p = 0.049). Accompanying the change in serum calcium, serum fetuin-A levels significantly increased (95.8 ± 45.8 pmol/mL at baseline to 124.9 ± 82.2 pmol/mL at 6 months, p = 0.043). CONCLUSIONS: Lowering dialysate calcium concentration significantly improved arterial stiffness parameters, which may have been associated with upregulation of serum fetuin-A.ope

Prediction of strawberry yield based on receptacle detection and Bayesian inference

The receptacle of strawberry is a more direct part than the flower for predicting yield as they eventually become fruits. Thus, we tried to predict the yield by combining an AI technique for receptacle detection in images and statistical analysis on the relationship between the number of receptacles detected and the strawberry yield over a period of time. Five major cultivars were cultivated to consider the cultivar characteristics and environmental factors for two years were collected to consider the climate difference. Faster R–CNN based object detector was used to estimate the number of receptacles per strawberry plant in given two-dimensional images, which achieved a mAP of 0.6587 for our dataset. However, not all receptacles appear on the two-dimensional images, and Bayesian analysis was used to model the uncertainty associated with the number of receptacles missed by the AI. After estimating the probability of fruiting per receptacle, prediction models for the total strawberry yield at the end of harvest season were evaluated. Even though the detection accuracy was not perfect, the results indicated that counting the receptacles by object detection and estimating the probability of fruiting per receptacle by Bayesian modeling are more useful for predicting the total yield per plant than knowing its cumulative yield during the first month

Prediction model for mechanical properties of lightweight aggregate concrete using artificial neural network

The mechanical properties of lightweight aggregate concrete (LWAC) depend on the mixing ratio of its binders, normal weight aggregate (NWA), and lightweight aggregate (LWA). To characterize the relation between various concrete components and the mechanical characteristics of LWAC, extensive studies have been conducted, proposing empirical equations using regression models based on their experimental results. However, these results obtained from laboratory experiments do not provide consistent prediction accuracy due to the complicated relation between materials and mix proportions, and a general prediction model is needed, considering several mix proportions and concrete constituents. This study adopts the artificial neural network (ANN) for modeling the complex and nonlinear relation between constituents and the resulting compressive strength and elastic modulus of LWAC. To construct a database for the ANN model, a vast amount of detailed and extensive data was collected from the literature including various mix proportions, material properties, and mechanical characteristics of concrete. The optimal ANN architecture is determined to enhance prediction accuracy in terms of the numbers of hidden layers and neurons. Using this database and the optimal ANN model, the performance of the ANN-based prediction model is evaluated in terms of the compressive strength and elastic modulus of LWAC. Furthermore, these prediction accuracies are compared to the results of previous ANN-based analyses, as well as those obtained from the commonly used linear and nonlinear regression models

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The finite element (FE) model updating is a commonly used approach in civil engineering, enabling damage detection, design verification, and load capacity identification. In the FE model updating, acceleration responses are generally employed to determine modal properties of a structure, which are subsequently used to update the initial FE model. While the acceleration-based model updating has been successful in finding better approximations of the physical systems including material and sectional properties, the boundary conditions have been considered yet to be difficult to accurately estimate as the acceleration responses only correspond to translational degree-of-freedoms (DOF). Recent advancement in the sensor technology has enabled low-cost, high-precision gyroscopes that can be adopted in the FE model updating to provide angular information of a structure. This study proposes a FE model updating strategy based on data fusion of acceleration and angular velocity. The usage of both acceleration and angular velocity gives richer information than the sole use of acceleration, allowing the enhanced performance particularly in determining the boundary conditions. A numerical simulation on a simply supported beam is presented to demonstrate the proposed FE model updating approach.clos