Concretes that contain binary-blended binders (BBB) and ternary-blended binders (TBB) incorporating thermally activated alum sludge ash (AASA), silica fume (SF), ground-granulated blast-furnace slag (GGBS) and palm oil fuel ash (POFA) are exposed to temperatures as high as 800 °C. The water-binder ratio of the multiple-blended binder (MBB) concretes was 0.30, and the total binder and polypropylene (PP) fibre contents were 493 and 1.8 kg/m3, respectively. The elevated temperature performance of the MBB concretes is evaluated in terms of the mass loss, compressive strength, ultrasonic pulse velocity (UPV) and surface cracks. The concrete strength deteriorated significantly due to elevated temperature up to 800 °C, but the residual strength of the BBB containing 15 % AASA was higher than that of the control and 20 % AASA concretes. High-temperature exposure decreased measured UPV values. The concrete weight loss was more pronounced for TBB concretes. The elevated temperature performance of all of the TBB concretes was better than that of the BBB concretes with the same AASA replacement levels. It was observed that PP fibres help reduce spalling. BBB concrete containing 15 % AASA combined with either SF or GGBS or POFA exhibits superior performance at elevated temperature than Portland cement concrete at the same mix design proportion.
Part of the book: High Performance Concrete Technology and Applications
This chapter utilizes artificial neural network (ANN) and multiple regression analysis (MRA) to model bridge condition rating based on limited number of data sets. Since data sets are very limited and there is a gap in range of rating scale, two conditions of data sets are used in this study, namely complete data sets and data set with bridge component condition rating data are missing. Five methods are then used to handle the missing bridge component condition rating data. Three commonly used methods and two new methods are explored in this study. It seems that the performance of the model using data sets after handling missing bridge component data to fill the gaps in the range scales of the bridge condition rating improved the performance of the model. In addition, a handling method that substitutes missing data of bridge component ratings with available bridge rating data is favorable. Based on the values of root mean square error (RMSE) and R2, the ANN models perform slightly better than MRA to map relationship between bridge components and bridge condition rating. This concluded that ANN is suitable to model bridge condition rating compare to MRA method.
Part of the book: Bridge Engineering