Identifying the influential factors in incident duration is important for traffic management agency to mitigate the impact of traffic incidents on freeway operation. Previous studies have proposed a variety of approaches to determine the significant factors for traffic incident clearance time. These methods commonly select a single “true” model among a majority of alternative models based on some model selection criteria. However, the conventional methods generally neglect the uncertainty related to the choice of models. This paper proposes a Bayesian Model Averaging (BMA) model to account for model uncertainty by averaging all plausible models using posterior probability as the weight. The BMA model is used to analyze the 2,584 freeway incident records obtained from I-5 corridor in Seattle, WA, USA. The results show that the BMA approach has the capability of interpreting the causal relationship between explanatory variables and clearance time. In addition, the BMA approach can provide better prediction performance than the Cox proportional hazards model and the accelerated failure time models. Overall, the findings in this study can be useful for traffic emergency management agency to apply an alternative methodology for predicting traffic incident clearance time when model uncertainty is considered.
Despite presenting a very high global warming toll, Portland cement concrete is the most widely used construction material in the world. The eco-efficiency, economy, and the overall mechanical and durability performances of concrete can be improved by incorporating supplementary cementitious materials (SCMs) as partial substitutions to ordinary Portland cement (OPC). Naturally found bentonite possesses pozzolanic properties and has very low carbon footprint compared to OPC. By applying activation techniques, the reactivity of bentonite can be improved, and its incorporation levels can be maximized. In this study, the influence of mechanical and thermo-mechanical activation of bentonite is investigated on properties of concrete. Bentonite was used for 0%, 10%, 15%, 20%, 25%, 30%, and 35% mass replacements of OPC. Mechanical (compressive strength and split tensile strength) and durability (water absorption, sorptivity coefficient, and acid attack resistance) properties were studied. Results of experimental testing revealed that, concrete containing bentonite showed good mechanical performance, while durability was significantly improved relative to control mix. Application of thermo-mechanical activation can enhance the incorporation levels of bentonite in concrete. At 15% and 25%, bentonite produced optimum results for mechanical and thermo-mechanical activation, respectively. Bentonite inclusion is more beneficial to the durability than the mechanical strength of concrete.
Abstract-Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.
We can also define this problem as: assigning a document D to some pre-determined set of categories |C|. Where pre-determined set of categories, C, is defined by equation 1.() In short, text classification is used for categorizing documents into predefined classes based on their contents. This is an automatic assignment process for text categorization. Text classification is the initial requirement of Text Retrieval Systems (TRS), which digs texts in response to user queries. Nowadays, different ML algorithms are in practice to manage and organize documents for Information Retrieval Systems (IRS) [4].
This paper presents an innovative approach towards the development of a green concrete. The geopolymer is an environmentally friendly construction/repairing material. In addition, glass fibers are helpful to influence the strength properties and to reduce hair line cracks and bleeding in concrete. This study is based on the use of fly ash and glass fibers as a partial replacement of cement and, subsequently, its effect on compressive strength and split tensile strength of concrete. The geopolymer is manufactured after the process of geopolymerization between class F fly ash and alkali activator fluid (sodium silicate and sodium hydroxide). In geopolymer concretes (GPC), an inorganic polymer called aluminosilicate will act as a binder, the same as conventional concrete has Portland cement (OPC)-generated C-S-H gel. The glass fibers are added in the ratios of 3%, 6%, and 10% by weight of cement. To check the effect of geopolymer and glass fibers on compressive strength and split tensile strength of concrete, concrete cubes of size 150 × 150 × 150 mm and concrete cylinders of size 150 × 300 mm with or without geopolymer and glass fibers were casted and cured for 7, 14, 21, and 28 days. The compressive strength and split tensile strength of all concrete cubes and cylinders were determined by compression testing machine. The findings of the research study revealed that concrete having geopolymer and glass fibers used as a partial replacement of cement showed lesser strength as compared to conventional concrete. Concrete having glass fibers showed reduced workability and more segregation as compared to geopolymer concrete and normal concrete. However, the concrete made either with geopolymer or glass fibers is economical as compared to conventional concrete.
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