PERFORMANCE EVALUATION OF LLDPE/CaCO3 MODIFIERS WITH AND WITHOUT TITANATE COUPLING AGENT ON ASPHALT BINDER AND MIXTURE

The complexity and challenge created by asphalt material motivates researchers and engineers to investigate the behavior of this material to develop a better understanding, and improve the performance of asphalt pavement. Over decades, a wide range of modification at macro, meso, micro and nano scales have been conducted to improve the performance of asphalt pavement. This study was initiated to utilize the newly developed asphalt modifier pellets. These pellets consisted of different combinations of calcium carbonate (CaCO3), linear low-density polyethylene (LLDPE) and titanate coupling agent (CA) to improve the asphalt binder as well as pavement performance across a wide range of temperature and loading pace. These materials were used due to their unique characteristics and promising findings from various industries, especially as modifiers in pavement material. The challenge is to make sure the CaCO3 disperses very well in the mixture. The rheological properties of neat asphalt binder PG58-28 and modified asphalt binder (PG58-28/LLDPE, PG58-28/CaCO3, PG58-28/CaCO3/LLDPE, and PG58-28/CaCO3/LLDPE/CA), were determined using rotational viscometer (RV) test, dynamic shear rheometer (DSR) test and bending beam rheometer test. In the DSR test, the specimens were evaluated using frequency sweep and multiple shear creep recovery (MSCR). The asphalt mixtures (aggregate/PG58-28, aggregate/ PG58-28/LLDPE, aggregate/PG58-28/CaCO3, aggregate/PG58-28/LLDPE/CaCO3 and aggregate/PG58-28/LLDPE/CaCO3/CA) were evaluated using the four point beam fatigue test, the dynamic modulus (E*) test, and tensile strength test (to determines tensile strength ratio, TSR). The RV test results show that all modified asphalt binders have a higher viscosity compared to the neat asphalt binder (PG58-28). Based on the Jnr results (using MSCR test), all the modified asphalt binders have a better resistance to rutting compared to the neat asphalt binder. A higher modifier contents have resulted in a better recovery percentage of asphalt binder (higher resistance to rutting), except the specimens prepared using PECC’s modified asphalt binder (PG58-28/CaCO3/LLDPE). The BBR test results show that all the modified asphalt binders have shown comparable performance in term of resistance to low temperature cracking, except the specimen prepared using the LLDPE modifier. Overall, 5 wt% LLDPE modified asphalt binder was found to be the best asphalt binder in terms of resistance to rutting. Meanwhile, 3 wt% PECC-1CA’s modified asphalt binder can be considered as the best (in terms of resistance to thermal cracking) with the lowest mean critical cracking temperature. The appearance of CaCO3 was found useful merely in improving the resistance to fatigue cracking of asphalt mixture. However, application of LLDPE has undermined the fatigue life of asphalt mixtures. Adding LLDPE and coupling agent throughout this study does not sufficiently help in terms of elastic behavior which essential to enhance the resistance to fatigue cracking. In contrast, application of LLDPE has increased the indirect tensile strength values and TSR of asphalt mixtures, indicates a better resistance to moisture damage. The usage of the coupling agent does not change the behavior of the asphalt mixture, which could be due to imbalance effects resulted by combination of LLDPE and CaCO3 in asphalt binder. Further investigations without incorporating CaCO3 should be conducted further. To investigate the feasibility of using LLDPE and coupling agent as modifiers in asphalt pavements, more research should be conducted on different percentages of LLDPE (less than 3 wt%), and at the higher and w wider range of coupling agent content, from 3 wt% to 7 wt% based on the polymer mass.

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user's memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors.