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2024 Vol.23, Issue 4 Preview Page

Research Article

Prediction of Road Construction Costs Using an ANN Algorithm

ANN 알고리즘을 활용한 도로 건설비용 예측

Jae-Young Her1
,
Seung-Joo Lee2
,
Chan-Young Park1
,
Young-Suk Kim3
,
Seung-Il Choi1
,
Sung-Lim Lee1
,
Yong-Seong Kim4*
허 재영1
,
이 승주2
,
박 찬영1
,
김 영석3
,
최 승일1
,
이 성림1
,
김 용성4*
1Member, Ph.D Student, Department of Regional Infrastructure Engineering, Kangwon National Univ., 1 Kangwondaehak-gil, Chuncheon, Gangwon-do 24341, Republic of Korea
2Member, Researcher, Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro Ilsanseo-Gu,Gyeonggi-Do 10223, Republic of Korea
3Member, Senior Research Fellow, Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro Ilsanseo-Gu,Gyeonggi-Do 10223, Republic of Korea
4Member, Professor, Department of Regional Infrastructure Engineering, Kangwon National Univ., 1 Kangwondaehak-gil, Chuncheon, Gangwon-do 24341, Republic of Korea
*Corresponding Author
31 December 2024. pp. 89-98
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Abstract

This paper proposes a construction cost prediction model through the application of Artificial Neural Networks (ANN) based on data available in the planning stage of road construction. To build the optimal ANN model, the predicted values of various input variables were compared with the actual construction costs using correlation coefficients and RMSE. The results indicate that the model utilizing total road length, road width, and design speed exhibited the best performance. An analysis of the relative importance of input variables on the predicted values showed that design speed had the most significant impact at 60.47%, while total road length had the lowest impact at 17.88%. This suggests that as design standards become more complex, construction costs increase accordingly. Thus, incorporating ANN-based predictions into future construction cost forecasting will enable efficient and reliable predictions that can account for increasingly complex design criteria.

Keywords
Artificial neural network
Construction cost prediction
Road design
Correlation coefficient
RMSE

본 연구에서는 도로 건설 계획 단계에서 활용 가능한 데이터를 바탕으로 인공신경망 기법을 통해 건설 비용 예측 모델을 제안하였으며, 최적의 인공신경망 모델 구축을 위해 다양한 입력변수별 예측값과 실제 건설 비용과 상관계수, RMSE 비교를 통해 최적의 건설 비용 예측 모델을 구축하였다. 연구 결과 도로 총연장, 도로의 폭, 설계속도를 활용한 모델이 가장 우수한 성능을 나타냈으며 입력변수가 예측값에 미치는 상대적 중요도를 분석한 결과 설계속도가 60.47%로 가장 크게 나타났고, 도로 총연장의 경우 17.88%로 가장 낮은 값을 나타내었다. 이는 설계 기준이 복잡할수록 건설비용이 증가하는 특성을 반영한 결과로 판단된다. 복잡 다단한 설계기준이 반영된 장래의 도로 건설 비용 예측에 본 연구를 통해 인공신경망 알고리즘을 이용하여 효율적이고 신뢰성 있는 도로 건설 비용 예측이 가능할 것이라 판단된다.

References
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Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 23
  • No :4
  • Pages :89-98
  • Received Date : 2024-11-07
  • Revised Date : 2024-12-16
  • Accepted Date : 2024-12-19
  • DOI :https://doi.org/10.12814/jkgss.2024.23.4.089
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