Mechanical properties of high-strength concrete subjected to high temperature by stressed test

Recently, the effects of high temperature on compressive strength and elastic modulus of high strength concrete were experimentally investigated. The present study is aimed to study the effect of elevated temperatures ranging from 20 ℃ to 700 ℃ on the material mechanical properties of high-strength concrete of 40, 60 and 80 MPa grade. During the strength test, the specimens are subjected to a 25% of ultimate compressive strength at room temperature and sustained during heating, and when the target temperature is reached, the specimens are loaded to failure. The tests were conducted at various temperatures (20-700 ℃) for concretes made with W/B ratios of 46%, 32% and 25%, respectively. The results show that the relative values of compressive strength and elastic modulus decrease with increasing compressive strength grade of specimen.

Removal of heavy metals using waste eggshell

The removal capacity of toxic heavy metals by the reused eggshell was studied. As a pretreatment process for the preparation of reused material from waste eggshell, calcination was performed in the furnace at 800℃ for 2 h after crushing the dried waste eggshell. Calcination behavior, qualitative and quantitative elemental information, mineral type and surface characteristics before and after calcination of eggshell were examined by thermal gravimetric analysis （TGA）, X-ray fluorescence （XRF）, X-ray diffraction （XRD） and scanning electron microscopy （SEM）, respectively. After calcination, the major inorganic composition was identified as Ca （lime, 99.63%） and K, P and Sr were identified as minor components. When calcined eggshell was applied in the treatment of synthetic wastewater containing heavy metals, a complete removal of Cd as well as above 99% removal of Cr was observed after 10 rain. Although the natural eggshell had some removal capacity of Cd and Cr, a complete removal was not accomplished even after 60 rain due to quite slower removal rate. However, in contrast to Cd and Cr, an efficient removal of Pb was observed with the natural eggshell rather than the calcined eggshell. From the application of the calcined eggshell in the treatment of real electroplating wastewater, the calcined eggshell showed a promising removal capacity of heavy metal ions as well as had a good neutralization capacity in the treatment of strong acidic wastewater.

Evaluation of properties of concrete using fluosilicate salts and metal (Ni,W) compounds

To improve watertightness and antibiosis of sewage structure concrete, the antimicrobial watertight admixture was made with fluosilicate salts and antimicrobial compounds. And fresh properties, watertightness, harmlessness and antibiosis of concrete were investigated experimentally. As a result, the fresh properties of concrete were similar to those of an ordinary concrete, without setting time delay. Compressive strength and carbonation resistance of concrete were better than those of an ordinary concrete. Finally it was confirmed that the antimicrobial watertight admixture of concrete had an antibiosis inhibiting SOB growth.

Remediation of Heavy Metals Polluted Soil Using Metal Insolubilizing Materials

Heavy metal ions, one kind of harmful substance, may exist in the soil irrelevant to artificial development, and soil contamination, due to soil and rock containing these naturally derived heavy metals, has recently become apparent. Thus, in an amendment that came into effect in 2010 of Japan, the scope of countermeasures and regulations for contaminated soil was amended to “contaminated soil derived from artificial development” and “naturally derived contaminated soil”. When naturally derived contaminated soil is encountered during the carrying out of construction work, countermeasures against this type of soil contamination are necessary. In this research, new metal-insolubilizing materials are developed in order to improve the insolubilization treatment which is one method for treating contaminated soil. Specifically, tests are conducted to clarify the insolubilization effect on heavy metals, and the insolubilization mechanism is chemically and mineralogically discussed.

Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.

Seismic hazard level reduction for existing buildings considering remaining building lifespans

Seismic hazard levels lower than those for design of new buildings have been permitted for seismic evaluation and retrofi t of existing buildings due to the relatively short remaining lifespans. The seismic hazard reduction enables costeff ective seismic evaluation and retrofi t of existing buildings with limited structural capacity. The current study proposes seismic hazard reduction factors for Korea, one of low to moderate seismicity regions. The seismic hazard reduction factors are based on equal probabilities of non-exceedance within diff erent remaining building lifespans. A validation procedure is proposed to investigate equality of seismic risk in terms of ductility-based limit states using seismic fragility assessment of nonlinear SDOF systems, of which retrofi t demands are determined by the displacement coeffi cient method of ASCE 41-13 for diff erent target remaining building lifespans and corresponding reduced design earthquakes. Validation result shows that the use of seismic hazard reduction factors can be permitted in conjunction with appropriate lower bounds of the remaining building lifespans.

Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

In this study,we examined the efficacy of a deep convolutional neural network(DCNN)in recognizing concrete surface images and predicting the compressive strength of concrete.A digital single-lens reflex(DSLR)camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN.Thereafter,training,validation,and testing of the DCNNs were performed based on the DSLR camera and microscope image data.Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy.The accuracy of the DSLR-derived image data was attributed to the relatively wider range of the DSLR camera,which was beneficial for extracting a larger number of features.Moreover,the DSLR camera procured more realistic images than the microscope.Thus,when the compressive strength of concrete was evaluated using the DCNN employing a DSLR camera,time and cost were reduced,whereas the usefulness increased.Furthermore,an indirect comparison of the accuracy of the DCNN with that of existing non-destructive methods for evaluating the strength of concrete proved the reliability of DCNN-derived concrete strength predictions.In addition,it was determined that the DCNN used for concrete strength evaluations in this study can be further expanded to detect and evaluate various deteriorative factors that affect the durability of structures,such as salt damage,carbonation,sulfation,corrosion,and freezing-thawing.

Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures.However,conventional manpower-based inspection methods not only incur considerable cost and time,but also cause frequent disputes regarding defects owing to poor inspections.Therefore,the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent,as the reduction in maintenance costs is significant from a long-term perspective.Thus,this paper proposes a deep learning-based image objectidentification method to detect the defects of paint peeling,leakage peeling,and leakage traces that mostly occur in underground parking lots made of concrete structures.The deep learning-based object-detection method can replace conventional visual inspection methods.A faster region-based convolutional neural network(R-CNN)model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface defects.The defects were classified according to their type,and the learning of each exclusive model was ensured through test sets obtained from real underground parking lots.As a result,average precision scores of 37.76%,36.42%,and 61.29%were obtained for paint peeling,leakage peeling,and leakage trace defects,respectively.Thus,this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.

利用不同植物降低夏季挥发性有机化合物的浓度(英文)

文中主要研究了植物过滤系统对改善室内空气品质的作用.通过实验比较了韩国本土和外来植物对室内空气污染物的净化作用.实验在夏季进行,将4种植物放入尺寸相同的样板房间,在相同实验条件下,对放入和未放入植物的样板房间内的苯、甲苯、乙苯、二甲苯和甲醛等有害物质的浓度加以监测,同时在植物放入3小时及3天后分别测定易挥发有机物(VOCs)的浓度;由此了解、比较和估计了两种外来植物和两种本土植物对易挥发有机化合物的净化效果.结果发现:(1)在所有实验中,置入植物的房间对VOCs物质的净化效果均比未置入植物的房间的大,而且外来植物对大多数VOCs物质的净化效果比本土植物更显著;(2)在种植和生长的空间实验中,本杰明橡胶树覆盖实验空间10%时净化效果最佳,该树对甲醛的净化效果尤其明显;(3)在种植和生长的空间实验中,盆栽金皇后种植在有阳光的地方时净化效果最佳,该植物对甲苯的净化效果最强.