Drainability and Clogging Behavior of Open-Graded Asphalt Friction Courses with Basic Oxygen Furnace Steel Slag Aggregates

Drainability of an open-graded asphalt friction course (OGAFC) is a primary functional requirement, which mainly depends on the presence of a network of interconnected air voids. These mixtures, however, are prone to clogging, which severely limits their drainability. Clogging predominantly occurs as a result of the deposition of external (sand, debris, and dust) and internal (stripped-off bitumen) materials into the pores of OGAFC, which is referred to as particle-related clogging. Another type of clogging is deformation-related clogging and it is observed mainly because of rutting along the wheel path. In this study, the drainability and clogging behavior of OGAFC mixes with basic oxygen furnace (BOF) steel slag as replacement of natural aggregates was studied. BOF steel slag was used as 0% (control mix), 25%, 50%, 75%, and 100% substitution for coarse natural aggregates in the preparation of OGAFC mixes with two types of modified binders. Three different clogging mechanisms: particle-related clogging (caused by intrusion of foreign material such as sand); stripping-related clogging (caused by the deposition of stripped-off bitumen-fines mortar), and deformation-related clogging (reduced drainability caused by permanent deformation) were considered in this study. Comparisons were made to investigate the effect of BOF steel slag on the clogging potential of OGAFC mixes. OGAFC specimens were evaluated for their drainage potential using a flexible-wall falling-head permeameter both before and after being subjected to various clogging environments. BOF-steel-slag-incorporated OGAFC mixes exhibited lower clogging potential and reported better performance in resisting clogging resulting from stripping and permanent deformation. </jats:p

Time series event correlation with DTW and Hierarchical Clustering methods

Data driven decision making is becoming increasingly an important aspect for successful business execution. More and more organizations are moving towards taking informed decisions based on the data that they are generating. Most of this data are in temporal format - time series data. Effective analysis across time series data sets, in an efficient and quick manner is a challenge. The most interesting and valuable part of such analysis is to generate insights on correlation and causation across multiple time series data sets. This paper looks at methods that can be used to analyze such data sets and gain useful insights from it, primarily in the form of correlation and causation analysis. This paper focuses on two methods for doing so, Two Sample Test with Dynamic Time Warping and Hierarchical Clustering and looks at how the results returned from both can be used to gain a better understanding of the data. Moreover, the methods used are meant to work with any data set, regardless of the subject domain and idiosyncrasies of the data set, primarily, a data agnostic approach.</jats:p

Investigation of Moisture Damage in Open Graded Asphalt Friction Course Mixtures with Basic Oxygen Furnace Steel Slag as Coarse Aggregate under Acidic and Neutral pH Environments

Open graded asphalt friction courses (OGAFCs) are specialty asphalt mixtures used to improve skid resistance and surface drainage. OGAFCs have additional benefits of reduced splash and spray, and lower tire–pavement interaction noise. Prolonged exposure to rainwater and load transfer through stone-on-stone contact in OGAFCs demands aggregates that are strong and hydrophobic. Rainwater acidity is expected to affect the aggregate–asphalt bond and thus moisture damage performance of OGAFC. This paper investigates the effect of rainwater acidity on moisture sensitivity of OGAFC mixtures with different aggregate types (natural aggregate, basic oxygen furnace (BOF) steel slag, and combinations of both) and modified binder types. For the first time, the present research reports the moisture damage potential of BOF OGAFC mixtures under different moisture conditioning environments created by varying the pH of contact water. With different combinations of BOF slag and natural aggregates (100:0, 25:75, 50:50, 75:25, and 0:100), and binders (polymer and crumb rubber modified), OGAFC mixtures were characterized for moisture damage through tensile strength ratio, wet Cantabro abrasion loss, and modified boiling water tests. Functional aspects of OGAFC mixtures subjected to moisture conditioning under different pH environments were also evaluated through permeability testing. Results showed that an acidic environment exacerbated the moisture damage, however, OGAFC mixtures containing BOF slag showed better performance than the control mixture (with natural aggregates only). Inclusion of BOF slag in OGAFC mixtures enhanced resistance to moisture damage under both pH environments. OGAFC mixes with 100% BOF slag content performed the best considering all moisture damage tests under both conditioning environments. </jats:p

Assessment of Student Mastery of Anticipated Learning Outcomes During a BlendFlex STEM CURE Using a Combination of Self-reported and Empirical Analysis

Due to the ongoing COVID-19 pandemic, institutions across the world have had to make modifications to existing curricula throughout the university. Course-based Undergraduate Research Experiences (CUREs) are emerging as an effective way to offer research opportunities for students traditionally underrepresented in STEM. These types of courses face the unique task of adapting scientific conceptual and research process concepts to the distance learning format. Biochemistry Authentic Student Inquiry Lab (BASIL) is a freely available, modular CURE. This project showcases a two-pronged approach which combines student self-reported mastery and objective evaluation of lab report responses, both aligned with established Anticipated Learning Outcomes (ALOs) for BASIL. Using pre- and post-surveys, we measured growth in knowledge, experience, and confidence (KEC) as a result of taking the course. Students reported learning more about bioinformatic experiments and concepts better than their wet-lab counterparts. KEC tied directly to ALOs had an average gain score of 67.0% while those referring to techniques increased 61.5%. Student lab report responses aligned to ALOs were analyzed on a Likert scale from one to five. Due to the emergency shift online, this analysis has provided preliminary data on the mastery of biochemical ALOs during online learning. The students’ mastery of wet-lab ALOs coincided with our findings that lab courses need enhanced strategies to teach critical STEM lab-research skills in an online setting. Such novel assessment strategies developed based on learning objectives help fill this skills gap and enhance the exposure of undergraduate students to vital STEM research experiences

Analysis of student mastery of anticipated learning outcomes during a BlendFlex STEM CURE using a combination of self-reported and empirical analysis

Universities are increasingly using Course-based Undergraduate Research Experiences (CURE) to offer research opportunities for students traditionally underrepresented in STEM. Broader adoption of this valuable learning model can be achieved by creating better defined assessment methods. In this study, the Biochemistry Authentic Student Inquiry Lab (BASIL) model is used to design a set of labs in order to answer established Anticipated Learning Outcomes (ALOs). We used a five-point Likert scale to analyze student lab report responses, assigning a higher score based on increasing order of mastery of the ALO. During this analysis, we developed guidelines for uniform scoring of these responses. We compared the obtained scores to a Participant Perception Indicator (PPI) survey, given to the lab students at the beginning and again at the conclusion of a biochemistry laboratory course. This survey asks students to rate their knowledge, experience, and confidence on these ALO statements and laboratory protocols that comprise the BASIL model. This combination of self-reported and empirical data is used to analyze whether the laboratory course has taught them the necessary skills and information to master the concepts represented by the ALOs. Students asked to rate their understanding of wet labs had a lower score in knowledge, confidence, and experience in comparison to computational methods. Overall, this decrease in understanding can be attributed to a lack of ability to experience wet-labs in person. This makes grasping and applying these concepts more difficult. These conclusions can help form enhanced strategies on how to teach these critical STEM lab-research skills in an online setting