scholarly journals Investigating Filler Characteristics in Upgrading Cold Bituminous Emulsion Mixtures

Author(s):  
Shakir Al-Busaltan

Abstract Cold Bituminous Emulsion Mixtures (CBEMs) could offer significant advantages in contrast to traditional Hot Mix Asphalt (HMA). These advantages are redaction of energy consumption, reduction of emission of pollutants, and reduction of total costs. To date, researchers have attempted, intensively, to upgrade CBEMs’ engineering characteristics to gain their whole advantages. Adding active filler materials such as Ordinary Portland Cement (OPC) develop these characteristics. In this paper, selected waste or by-product materials are investigated as alternatives to OPC. Although OPC alone or with activator has proven successful in improving curing time and mechanical properties, the successful use of waste or by-product alternatives could represent a unique environmental and economic achievement. Thus, for the first time, waste or by-product materials (PFA, PSA, GGBFS, APC, and BFA) were investigated individually and not as Supplementary Cementitious Material (SCM) for improving the curing and strength of CBEM. However, some of these filler showed significant improvement in CBEMs characteristics.

2020 ◽  
Vol 11 (5) ◽  
pp. 214
Author(s):  
Sajjad Ali Mangi ◽  
Zubair Ahmed Memon ◽  
Shabir Hussain Khahro ◽  
Rizwan Ali Memon ◽  
Arshad Hussain Memon

2021 ◽  
pp. 107754632110033
Author(s):  
Gang Xiao ◽  
Qinwen Yang ◽  
Fan Yang ◽  
Tao Liu ◽  
Tao Li ◽  
...  

Automatic driving of trains can significantly reduce the energy cost and enhance the operating efficiency and safety. The automatic train driving system has to be an embedded system that can run onboard with low power, which necessitates an efficient inference model. In this article, a level-wise driving knowledge induction approach is proposed for embedded automatic train driving systems. The coincident driving patterns in the records of drivers with different experience levels suggest the suitability of a driving experience knowledge rule induction approach. We design a two-level learning approach to obtain both the driving experience pattern in fuzzy rule-based knowledge form and the detailed parameters of velocity and gear by regression learning methods. With 8.93% energy consumption reduction compared with average human drivers, the experiments indicate the effectiveness of our approach.


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