• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.752-759
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• 2006

Conglomerates and core-stones are the typical composite geomaterials that are composed of a weak matrix and a strong pebble part. In general, we couldn't analyze the composite geomaterials by using emperical rock classification methods. In the study, a series of analyses of elastic behavior of composite geomaterials are carried out by using homogenization theory. 45-case models are made with considering 3 kind of factors such as gravel content, size and strength of matrix. Those are applicable to various composite geomaterials of conglomerates and core-stones. The size of analysis model is large enough to exceed REV.

• Advances in concrete construction
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• v.6 no.3
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• pp.285-296
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• 2018

In the present paper, the results of an experimental study of the bond between repair materials and mortar substrate subjected to hot climate is presented. Half-prisms of size $40{\times}40{\times}80mm$, serving as a substrate mortar samples (SUBM) were manufactured in the laboratory and then stored at an ambient temperature for 6 months. Five self compacting mortar mixes (SCMs) incorporating 0%, 10%, 20%, 30%, and 40% of natural pozzolana as white cement replacement were used as repair materials. Repaired composite samples (SCMs/SUBM) were cured at hot climate for different lengths of time (28 and 56-days). During the first week of curing, the composite samples were watered twice a day. The test carried out to assess the bond between SCMs and SUBM was based on three-point bending (3 PB) test. The obtained results have proved that it was feasible to produce compatible repair materals in this curing environment by using up to 30% natural pozzolana as white cement replacement.

• Steel and Composite Structures
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• v.49 no.2
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• pp.161-180
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• 2023

In the previous research, the axial compressive capacity models for the glass fiber-reinforced polymer (GFRP)-reinforced circular concrete compression elements restrained with GFRP helix were put forward based on small and noisy datasets by considering a limited number of parameters portraying less accuracy. Consequently, it is important to recommend an accurate model based on a refined and large testing dataset that considers various parameters of such components. The core objective and novelty of the current research is to suggest a deep learning model for the axial compressive capacity of GFRP-reinforced circular concrete columns restrained with a GFRP helix utilizing various parameters of a large experimental dataset to give the maximum precision of the estimates. To achieve this aim, a test dataset of 61 GFRP-reinforced circular concrete columns restrained with a GFRP helix has been created from prior studies. An assessment of 15 diverse theoretical models is carried out utilizing different statistical coefficients over the created dataset. A novel model utilizing the group method of data handling (GMDH) has been put forward. The recommended model depicted good effectiveness over the created dataset by assuming the axial involvement of GFRP main bars and the confining effectiveness of transverse GFRP helix and depicted the maximum precision with MAE = 195.67, RMSE = 255.41, and R2 = 0.94 as associated with the previously recommended equations. The GMDH model also depicted good effectiveness for the normal distribution of estimates with only a 2.5% discrepancy from unity. The recommended model can accurately calculate the axial compressive capacity of FRP-reinforced concrete compression elements that can be considered for further analysis and design of such components in the field of structural engineering.