- 期刊
Stiffness Prediction of Recycled Aged CRM Binders Using an Artificial Neural Network
並列摘要
This research explores the utilization of an artificial neural network (ANN) in predicting the stiffness of recycled aged binders containing crumb rubber modifier (CRM). The data were organized into six independent variables (rotational viscosity of unaged, high failure temperatures of unaged and RTFO (rolling thin film oven) residual, and large molecular sizes of unaged, RTFO residual, and RTFO+PAV (pressure aging vessel) residual) covering the binder properties and one dependent variable, the binder stiffness. The training and testing results showed that the model explains 0.943 of the variability in stiffness, indicating that the ANN techniques are effective in predicting the stiffness of recycled aged CRM binders tested in this study.
參考文獻
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被引用紀錄
彭信穎（2012）。氮氣誘導可調發光奈米石墨烯薄電子結構之研究〔碩士論文，淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2012.00588
蔡宗穎（2010）。平面方波式微混合器中運用邊界凸出塊之流場研析〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841/NTUT.2010.00640
戴吉煌（2006）。混合分波多工光纖網路傳輸效能之提昇〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841/NTUT.2006.00256
楊儒易（2011）。熱點導向模型式光學鄰近效應修正方法〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu201100556
李慕溪（2011）。利用動態光學微影分析進行規則式光學鄰近效
應修正〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu201100555
延伸閱讀
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- Chandwani, V., Agrawal, V., & Nagar, R. (2014). Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks. Advances in Artificial Neural Systems, 2014(), h1-9. https://doi.org/10.1155/2014/629137
- 張哲維（2001）。Application of the Artificial-Neural-Network to Finite Element Model Updating〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu200100408
- Chen, B. (2017). AUC oriented Bidirectional LSTM-CRF Models to Identify Algorithms Described in an Abstract [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU201702449
- Jin, H. P., Yang, W. Y., & Yan, L. (2012). Determination of Residual Stresses and Material Properties by an Energy-Based Method Using Artificial Neural Networks. Proceedings of the Estonian Academy of Sciences, 61(4), 296-305. https://www.airitilibrary.com/Article/Detail?DocID=17367530-201212-201310180036-201310180036-296-305