Dr. Md Abdul Karim

This study is a preliminary investigation of the independent utilization of two types of fly ash (FA)–FA Type C and FA Type F-as partial replacement of fine aggregate (sand) and cement in Portland cement concrete (PCC) mixes. The main objective was to determine an optimum substitution range for each type of FA that would offer well-performing concrete in terms of workability, compressive strength, and durability. To this end, multiple concrete batches were prepared, incorporating each type of FA at four different levels: 5%, 10%, 15%, and 20% by weight of fine aggregate replacement and 10%, 20%, 30%, and 40% by weight for cement replacement. Then, concrete samples (100 mm diameter × 200 mm tall cylinders) were cast from each batch and were moisture-cured for 7, 14, and 28 days prior to testing. The addition of FA contributed positively to the strength development at specific replacement levels: all percentages for both FA Type C and Type F for fine aggregate replacement and up to 30% FA content for both Type C and F for cement replacement, 10% for both FA Type C and Type F provided the higher strength for aggregate replacement, and 10–20% for both types of FA provided the higher strength for cement replacement. Furthermore, these additions of FA exhibited comparable workability and durability except for FA Type F, which did not exhibit comparable workability for aggregate replacement. FA Type C can be recommended for both early and long-term strength for fine aggregate replacement, whereas FA Type C is suggested to be used for early strength and Type F provides for long-term strength for cement replacement. Type C provides better durability and Type F provides better workability for cement replacement.

During COVID-19 intervention, all the university courses were offered online either asynchronous or synchronous delivery mode. After COVID-19 intervention, most of the universities are trying to go back on face-to-face delivery mode for all levels of courses with the perception that on campus attendance is important for students’ learning performance and campus experience. In this study only the impact of university students’ class attendance on learning performance was investigated. Data from multiple environmental engineering courses taught by the author in a university were used where attendance in classes was not mandatory. All of the courses were junior and senior level courses in engineering programs. Only the data for students who enrolled in the courses, physically attended or not attended classes as well as took all three-midterm exams were used. Cluster analysis and trend analysis methods were used to analyze the data and conclude the findings. Analysis of the data demonstrated no remarkable positive correlation of the students’ learning performance with attendance. In junior level courses more clustered were shown above the 45-degree line compared to senior level courses. Also, in some cases in junior level courses it showed slight positive correlation whereas in senior level courses it showed zero to negative correlation. In terms of weighted average GPA analysis, no statistically significant differences were observed among online, hybrid and F2F delivery modes for all junior and senior level courses. It is obvious that attendance does not have any impact on senior level courses. So, senior level courses can be offered online or hybrid with confidence without losing any student learning.

This study explored the independent utilization of five common waste materials - rubber, plastic, glass, slag, and sewage sludge ash (SSA) - as partial replacement of fine aggregate in Portland cement concrete (PCC) mixes. The main objective was to determine an optimum substitution range for each waste material that would offer well performing concrete in terms of workability, compressive strength, and durability. To this end, multiple concrete batches were prepared, incorporating each waste material at four different levels: 5%, 10%, 15%, and 20% by weight of fine aggregate. Then, concrete samples (100-mm diameter × 200-mm tall cylinders) were cast from each batch and were moisture-cured for 7, 14, and 28 days prior to testing. Also, the chemical composition of each waste material was identified using FTIR spectroscopy to understand its impact on the development of concrete strength. While most waste led to the diminished strength gains at all substitution rates, the addition of glass, slag, and SSA contributed positively to the strength development at specific replacement levels: 5% for glass, 10 - 15% for slag, and 5 – 15% for SSA. Furthermore, these additions of glass and slag exhibited comparable workability and durability although SSA did not exhibit comparable workability and durability. The findings of this study can hold significant implications for environmental sustainability and cost effectiveness in construction projects.

Independent study called “Undergraduate Research” at our university is a highly effective method to inspire students in scholarly work through research. A literature review underscores the manifold benefits of independent study/undergraduate research, including enhanced academic performance, increased motivation and confidence, heightened awareness of students’ limitations, and improved self-management skills. Notably, this approach allows teachers to tailor tasks to individual students, fostering social inclusion and mitigating feelings of alienation. Another inspiring factor for students is the flexibility afforded by not having to attend traditional for 3 hours per week in-class lecture to earn course credit. Over the years, the authors have implemented this course within the engineering disciplines of the authors, diligently collecting data on the student perceptions and attitudes towards independent study through a questionnaire survey via Qualtrics. The survey questions were strategically designed to explore the benefits of learning, the long-term retention of acquired knowledge, and the overall learning processes. Analysis of the data demonstrated a positive student perception and attitude towards a few crucial skills, such as teamwork and time management, technical writing and subject matter proficiency, Excel uses, data analytics, communications, and organizational timeline skills. Students expressed a preference for the hands-on aspect and freedom associated with the undergraduate research. Furthermore, students acknowledged the significant influence of undergraduate research on their academic careers, citing improved understanding of their chosen fields, and a heightened interest in pursuing graduate school.

An innovative building design technology is being modelled to kill all pathogens including COVID-19 inside the building naturally before it attacks the human body. In this study, a solar irradiance has been applied through an exterior glazing wall of the building to create ultraviolet germicidal irradiation (UVGI), which is derived from sunlight, to destroy all pathogens inside the buildings before they attack human bodies. Research shows that all pathogens, including COVID-19, can be destroyed by UVGI's short-range wavelengths of 254-280 nanometers by rupturing their nucleic acid bonds, forcing them to malfunction their biochemical operations and ultimately causing the pathogen to die which indeed be an innovative field of science to eliminate pathogen naturally before it penetrates to the human body.

The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security-sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models' performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNN's accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.

In-class problem-solving in the field of science and engineering is one of the active learning and time-demanding approach to engaging students in activities in face-to-face class settings and online environments. The origin of learning is rooted in the activity, which is doing something to find out about specific topics. Engineering and science students are trained to design and construct solutions to problems in the real world. This paper presents the perceptions and attitudes of students who participated in in-class problem-solving activities in an environmental engineering course in several semesters. One of the courses from the Civil and Environmental Engineering curriculum, Introduction to Environmental Engineering was used to verify whether in-class problem-solving activities help students learn and improve their overall course grades. Problem-solving in the class as a part of course delivery was performed in each topic of the courses. At the end of the semester, a survey with three Likert-scale questions was conducted, and the data will be analyzed to determine the students’ perceptions and attitudes about the activity in terms of their learning experience and performance in the exam. The final grades will also be analyzed statistically and compared with previous similar semesters’ data to predict the effect of in-class problem-solving activities.

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country's social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country's 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2 nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score.