• Geomechanics and Engineering
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• 제8권4호
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• pp.615-630
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• 2015

The use of helical anchors has been extensively beyond their traditional use in the electrical power industry in recent years. They are commonly used in more traditional civil engineering infrastructure applications so that the advantages of rapid installation and immediate loading capability. The majority of the research has been directed toward the tensile uplift behaviour of single anchors (only one plate) by far. However, anchors commonly have more than one plate. Moreover, no thorough numerical and experimental analyses have been performed to determine the ultimate pullout loads of multi-plate anchors. The understanding of behavior of these anchors is unsatisfactory and the existing design methods have shown to be largely inappropriate and inadequate for a framework adopted by engineers. So, a better understanding of helical anchor behavior will lead to increased confidence in design, a wider acceptance as a foundation alternative, and more economic and safer designs. The main aim of this research is to use numerical modeling techniques to better understand multi-plate helical anchor foundation behavior in soft clay soils. Experimental and numerical investigations into the uplift capacity of helical anchor in soft clay have been conducted in this study. A total of 6 laboratory tests were carried out using helical anchor plate with a diameter of 0.05 m. The results of physical and computational studies investigating the uplift response of helical anchors in soft clay show that maximum resistances depend on anchor embedment ratio and anchor spacing ratio S/D. Agreement between uplift capacities from laboratory tests and finite element modelling using PLAXIS is excellent for anchors up to embedment ratios of 6.

• 한국구조물진단유지관리공학회 논문집
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• 제21권3호
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• pp.76-85
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• 2017

본 연구에서는 유압식 급속재하 시험 장치를 제작하여 변형 속도에 따른 후크형 강섬유 및 폴리아미드 섬유보강 시멘트 복합체의 압축강도 및 인장강도 특성을 평가하였다. 그 결과, 변형 속도가 증가함에 따라 압축강도, 최대 응력 점에서의 변형 및 탄성계수는 증가하였으며, 섬유 종류 및 혼입률은 변형 속도에 의한 압축강도의 영향은 크지 않았다. 본 연구에서 평가된 압축강도의 DIF는 CEB-FIP model code 2010에 비해 상회하였으며, ACI-349의 예측값과 유사한 경향이 나타났다. 인장특성의 경우에도 변형 속도가 증가함에 따라 인장강도와 변형능력이 크게 향상되었다. 후크형 강섬유보강 시멘트 복합체는 변형 속도가 증가함에 따라 섬유와 매트릭스의 부착력이 증가하는 것에 의해 인장강도와 변형능력이 크게 향상되었으며, 섬유가 매트릭스로부터 인발되는 파괴 특성이 나타났다. 한편, 폴리아미드 섬유보강 시멘트 복합체의 경우 섬유와 매트릭스의 부착력이 크기 때문에 섬유가 매트릭스로부터 인발되지 않고 끊어지는 파괴 특성이 나타났으며, 폴리아미드 섬유보강시멘트 복합체의 인장특성에 대한 변형 속도 효과는 섬유의 인장강도에 큰 영향을 받는 것으로 판단되었다. 이러한 결과로부터 폴리아미드 섬유보강 시멘트 복합체의 인장강도에 대한 변형 속도의 효과는 후크형 강섬유의 부착력에 대한 민감도 보다 큰 것으로 사료된다.

• Computers and Concrete
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• 제22권2호
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.