PURPOSES : Construction cost estimates are important information for business feasibility analysis in the planning stage of road construction projects. The quality of current construction cost estimates are highly dependent on the expert's personal experience and skills to estimate the arithmetic average construction cost based on past cases, which makes construction cost estimates subjective and unreliable. An objective approach in construction cost estimation shall be developed with the use of machine learning. In this study, past cases of road projects were analyzed and a machine learning model was developed to produce a more accurate and time-efficient construction cost estimate in teh planning stage. METHODS : After conducting case analysis of 100 road construction, a database was constructed including the road construction's details, drawings, and completion reports. To improve the construction cost estimation, Mallow's Cp. BIC, Adjusted R methodology was applied to find the optimal variables. Consequently, a plannigs-stage road construction cost estimation model was developed by applying multiple regression analysis, regression tree, case-based inference model, and artificial neural network (ANN, DNN). RESULTS : The construction cost estimation model showed excellent prediction performance despite an insufficient amount of learning data. Ten cases were randomly selected from the data base and each developed machine learning model was applied to the selected cases to calculate for the error rate, which should be less than 30% to be considered as acceptable according to American Estimating Association. As a result of the analysis, the error rates of all developed machine learning models were found to be acceptable with values rangine from 17.3% to 26.0%. Among the developed models, the ANN model yielded the least error rate. CONCLUSIONS : The results of this study can help raise awareness of the importance of building a systematic database in the construction industry, which is disadvantageous in machine learning and artificial intelligence development. In addition, it is believed that it can provide basic data for research to determine the feasibility of construction projects that require a large budget, such as road projects.

실적공사비 적산방식은 품셈견적, 실측견적, 단가견적, 및 총액견적 등 매우 다양하다. 표준품셈은 공공기관 및 민간기관의 공사비 책정기준이 되는 자료이다. 본 논문에서는 도로공사에 이용되는 기존 품셈견적의 문제점을 개선하기 위해 현장조사를 실시하고 분석하였으며 그 결과로 각 공종에 대한 실측견적 방법을 통계적 방법을 통해 제시하였다. 또한, 기존 품셈과 실측품셈을 공사단위의 비교를 통해 제안된 품셈이 보다 간단한 견적 작성을 가능하게 하고 보다 현실적인 공사금액을 산출함을 알 수 있었다. 본 연구에서는 보조기층의 Case-Study를 통하여 각 방식별 장단점을 가시적으로 비교해 보았다. 실측품셈으로 적용하였을 경우, 계산과정은 기존품셈의 50%로 축소되었으며 1일 1장비 사용으로 기존의 1일미만 장비 사용에 대한 편차가 보완되었다. 또한 품셈만을 이용하여 공정의 시공내용을 짐작하고 이를 바탕으로 공정계획이나 인력투입계획 등을 수립할 수 있었다.

Introduction of construction cost estimating system is necessary to promote appropriate reflection of construction cost and simplified and efficient amount work. The results of this study are as follows. In the results of considering the basic concept and composition of a construction type estimating system, an example orders are concentrated on an apartment house in the country. The building appurtenant work of extension work is high(1.52) as compared with others. In regression analysis for a construction cost, the models are as follows. In a new construction work, (construction cost)=(building area), and in extension work, (construction cost)=(building area). Accordingly, this study wishes to compare and analyzes main contents of original cost method and results cost method, and propose predetermined amount estimation device through existent literature study investigation for accumulation of the construction cost.