• Journal of the Korean Geosynthetics Society
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• v.10 no.4
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• pp.123-130
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• 2011

In the precedent study it was presented that the comparison of thermal resistivity using various backfill materials including river sand regarding water content, dry unit weight and particle size distribution. Based on the precedent study, this study focused on developing the optimized backfill material that would improve the power transfer capability and minimize the thermal runaway due to an increase of power transmission capacity of underground power cables. When raw materials, such as river sand, recycled sand, crush rock and stone powder, are used for a backfill material, they has not efficient thermal resistivity around underground power cables. Thus, laboratory tests are performed by mixing Fly-ash, slag and floc with them, and then it is found that the optimized backfill material are required proper water content and maximum density. Through various experimental test, when coarse material, crush rock, is mixed with recycled sand, stone powder, slag or floc for a dense material, the thermal resistivity of it has $50^{\circ}C$-cm/Watt at optimum moisture content, and the increase of thermal resistivity does not happen in dry condition. The result of experiments approach the optimization of the backfill materials for underground power cables.

• Geomechanics and Engineering
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• v.24 no.3
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• pp.237-251
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• 2021

The main purpose of this study is to investigate the performance of the proposed hybrid teaching-learning based optimization algorithm on the optimum design of reinforced concrete (RC) cantilever retaining walls. For this purpose, three different design examples are optimized with 100 independent runs considering continuous and discrete variables. In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful meta-heuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms. Moreover, Rao-1 and Rao-2 algorithms are applied to this design problem for the first time. The objective function is defined as minimizing the total material and labor costs including concrete, steel, and formwork per unit length of the cantilever retaining walls subjected to the requirements of the American Concrete Institute (ACI 318-05). Furthermore, the effects of peak ground acceleration value on minimum total cost is investigated using various stem height, surcharge loads, and backfill slope angle. Finally, the most robust results were obtained by HTLBO with 50 populations. Consequently the optimization results show that, depending on the increase in PGA value, the optimum cost of RC cantilever retaining walls increases smoothly with the stem height but increases rapidly with the surcharge loads and backfill slope angle.

• Journal of the Korea Concrete Institute
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• v.12 no.1
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• pp.113-125
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• 2000

The purpose of this study was to examine the durability characteristics of controlled low strength material(flowable fill) with high volume fly ash content. Flowable fill refer to self-compacted, cementitious material used primarily as a backfill in lieu of compacted fill. The two primary advantages of flowable fill over traditional methods are its ease of placement and the elimination of settlement. Therefore, in difficult compaction areas or areas where settlement is a concern, flowable fill should be considered. The fly ash used in this study met the requirements of KS L 5405 and ASTM C 618 for Class F material. The mix proportions used for flowable fill are selected to obtain low-strength materials in the 10 to 15kgf/$\textrm{cm}^2$ range. The optimized flowable fill was consisted of 60kg f/$\textrm{m}^3$ cement content, 280kgf/$\textrm{m}^3$ fly ash content, 1400kgf/$\textrm{m}^3$ sand content, and 320kgf/$\textrm{m}^3$ water content. Subsequently, durability tests including permeability, warm water immersion, repeated wetting & drying, freezing & thawing for high volume fly ash-flowable fill are conducted. The results indicated that flowable fill has acceptable durability characteristics.

• Structural Engineering and Mechanics
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• v.57 no.4
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• pp.763-783
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• 2016

A methodology based on Teaching Learning-Based Optimization (TLBO) algorithm is proposed for optimum design of reinforced concrete retaining walls. The objective function is to minimize total material cost including concrete and steel per unit length of the retaining walls. The requirements of the American Concrete Institute (ACI 318-05-Building code requirements for structural concrete) are considered for reinforced concrete (RC) design. During the optimization process, totally twenty-nine design constraints composed from stability, flexural moment capacity, shear strength capacity and RC design requirements such as minimum and maximum reinforcement ratio, development length of reinforcement are checked. Comparing to other nature-inspired algorithm, TLBO is a simple algorithm without parameters entered by users and self-adjusting ranges without intervention of users. In numerical examples, a retaining wall taken from the documented researches is optimized and the several effects (backfill slope angle, internal friction angle of retaining soil and surcharge load) on the optimum results are also investigated in the study. As a conclusion, TLBO based methods are feasible.