• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.167-168
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• 2023

Construction of reinforced concrete structures required massive amounts of concrete and steel rebar. The current procedure to estimate the quantity of rebar requires tedious and time-consuming manual labor. Consequently, this circumstance made the engineers vulnerable to error and mistake, which led to the rebar waste. No system that is capable of automatically calculating rebar length has yet been developed Thus, this study proposes a preliminary investigation of automatic rebar length estimation of beam element by using BIM-based shape codes drawn in Revit. Beam is chosen due to its complexity in the rebar arrangement. In addition, the development of this study could assist engineers on the construction site and effectively contribute to the minimization of rebar waste in the future.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.132-133
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• 2015

Efficient scheduling and resource management are the key factor to reduce construction project budget (e.g., labor cost, equipment cost, material cost, etc.). Resource-based line of balance (LOB) technique has been used to complement the limitations of time-driven scheduling techniques (e.g., critical-path method). Optimizing construction alternatives contributes cost savings while honoring the project deadline. However, existing LOB scheduling is lack of identifying optimal resource combination. This study presents a method which identifies the optimal construction alternatives, hence achieving resource minimization in a repetitive construction by using genetic algorithm (GA). The method provides efficient planning tool that enhances the usability of the system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.152-153
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, six influential factors (Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. After using algorithm of various methods of machine learning techniques, we selected the most suitable regression analysis model for estimating the compressive strength.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.83-84
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, seven influential factors (W/B ratio, Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. The purpose of this paper is to estimate compressive strength more accurately by applying it to algorithm of the Deep learning.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.185-186
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• 2015

The Standard penetration test determines the type of soil according to soil bearing capacity, and this classifies the subsoil into many layers. Construction project managers are willing to know the depth of the present types of subsoil on site in order to make plans on earthwork stage during excavation. However the standard penetration test may not provide accurate information on subsoil type due to incorrect spacing. To solve this problem, this study propose a conceptual algorithm for determining the spacing of standard penetration test spots to essentially tests relevant locations on which to be applied the standard penetration test. This provides the acquirement of the accurate layered model volume of earthwork revised into geological columnar section. This algorithm will determine the appropriate standard penetration test spots spacing on a given size of site to optimize the accuracy of the earthwork volume, time and cost.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.21-22
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• 2023

In reinforced concrete constructions, reinforcing bar generates more CO₂ per unit weight than other construction materials. In particular, cutting and bending rebar is the main source of rebar waste in the construction industry. Rebar-cutting waste is inevitable during the construction of a reinforced concrete structure since the rebar is not manufactured as designed. Large amounts of waste can be avoided by utilizing optimal cutting patterns and schedules. This research provides a fundamental analysis of the automatic calculation of column rebar length using BIM-based shape codes to minimize cutting waste to near zero. By employing this approach in practice, it is possible to minimize the rate of rebar-cutting waste, reduce costs, shorten construction duration, and reduce CO₂ emissions. In addition, the development of this research will serve as a clue for the development of BIM-based rebar layout automation algorithms.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.166-167
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• 2016

Unlike the past, supported by the development of digital technologies, free-form buildings are frequently designed with creative thoughts of architectural designers. However, there are some difficulties preventing successfully completion of projects, like reduced productivity and increased construction duration and cost upon the process of producing and installing concrete panels for free-form structures. In particular, there are active studies on the CNC machine for production of free-form concrete panels. Yet, it is difficult to effectively and easily come up with information on production and installation of free-form, curve-surfaced panels which are difficult to be mathematically defined. This requires a lot of manpower and time to implement the curved surfaces of free-form buildings as intended by architects. Accordingly, it needs a model that can effectively create production-installation data of free-form concrete panels for successful free-form building projects. Thus, the purpose of the study is to suggest data estimation model of production-installation using mathematical algorithm in free-form concrete panels. The study results will realize effective production and installation of free-form concrete members, allowing improved productivity of projects, reduced cost and shortened construction duration.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.79-80
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• 2023

Reinforced concrete structures require large amounts of concrete and rebar in the construction stage. Rebar is a major resource for reinforced concrete structures, and generates more CO₂ per unit weight than other materials. To solve this problem, it was confirmed that the cutting waste can be close to zero when the special length of the rebar is calculated in the drawing created after structural design. However, a system for automatically calculating the length of reinforcing bars to efficiently calculate the total amount of reinforcing bars has not been established. Therefore, the objective of this study is a basic study of automatic rebar length estimate algorithm of bearing wall by using BIM-based shape codes built in Revit. The bearing wall rebar can be automatically derived using the developed model. Furthermore, through applying the developed model to the construction field, it will greatly contribute to reducing greenhouse gas emissions by reducing rebar cutting waste.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.333-335
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• 2015

A computer vision-based scene recognition algorithm is proposed for monitoring construction sites. The system analyzes images acquired from a surveillance camera to separate regions and classify them as building, ground, and hole. Mean shift image segmentation algorithm is tested for separating meaningful regions of construction site images. The system would benefit current monitoring practices in that information extracted from images could embrace an environmental context.