아스팔트 층 두께 예측을 위한 통계적 모형과 기계학습 기법 활용에 대한 연구 Use of statistical models and machine-learning techniques to predict asphalt layer thickness
pp.63-72
This study aimed to improve the accuracy of road pavement design by comparing and analyzing various statistical and machine-learning techniques for predicting asphalt layer thickness, focusing on regional roads in Pakistan. The explanatory variables selected for this study included the annual average daily traffic (AADT), subbase thickness, and subgrade California bearing ratio (CBR) values from six cities in Pakistan. The statistical prediction models used were multiple linear regression (MLR), support vector regression (SVR), random forest, and XGBoost. The performance of each model was evaluated using the mean absolute percentage error (MAPE) and root-mean-square error (RMSE). The analysis results indicated that the AADT was the most influential variable affecting the asphalt layer thickness. Among the models, the MLR demonstrated the best predictive performance. While XGBoost had a relatively strong performance among the machine-learning techniques, the traditional statistical model, MLR, still outperformed it in certain regions. This study emphasized the need for customized pavement designs that reflect the traffic and environmental conditions specific to regional roads in Pakistan. This finding suggests that future research should incorporate additional variables and data for a more in-depth analysis.
1. 서론
2. 역학적-경험적 아스팔트 도로포장 설계
2.1. 교통하중
2.2. 환경하중
2.3. 재료물성
2.4. 도로포장 공용성 산정
3. 분석 모형
3.1. 다중선형회귀모형
3.2. 서포트 벡터 회귀(Support Vector Regressionanalysis, SVR)
3.3. 랜덤 포레스트(Random Forest)
3.4. XGboost
4. 예측력 검증
4.1. k겹 교차검증(k-fold cross-validation)
5. 분석결과
5.1. 예측결과
5.2. 다중회귀모형 결과
5.3. 변수중요도
6. 결론
REFERENCES