A new approach that adopts the use of fuzzy adaptive resonance theory (ART) neural network in estimating high-performance concrete (HPC) mix proportion from experimental data is devised. The proposed model receives a set of desired concrete performances, searches for a set of mix proportions that is near to the desired concrete performances, classifies the mix proportions into clusters, measures the similarity between performances of deduced clusters with desired performances, and deduces a mix proportion. The proposed model was used to estimate the mix proportions of five batches of concrete based on the performance criteria of 7th and 28th day compressive strengths. The generated mix proportions were used in an experimental work and the errors were within 13% for 7th compressive strength; and 7% for the 28th day compressive strength, signifying the reliability of the fuzzy ART-based model in estimating the mix proportion of HPC. This article contributes to an alternative method of mix proportion estimation of HPC by avoiding the use of complicated function approximation techniques. NOTATIONSThe following symbols are used in this article: FDesired concrete performances ε Tolerance range q m

Ag2Se is a ductile material with low lattice thermal conductivity that can be a valuable substitute to both PbSe and Bi2Se3 for Pb toxicity free and Bi scarcity. However, its...

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Previous studies highlighted the challenges in assessing psychomotor skills in engineering education when using online platforms. The main aim of this study was to examine the effectiveness of learning psychomotor skills online in Civil Engineering Design Project during the pandemic. This paper discusses the challenges faced in assessing the psychomotor domain in Civil Engineering Design Project during the pandemic and the immediate actions taken during the semester. New structural engineering software was used as the initial software was not accessible outside campus. The greatest challenge for lecturers was to learn the new software within a short period and to implement the use of the new software in their teaching and learning activities as well as in the students’ assessments. New evaluation rubrics were created to assess students’ psychomotor skills in using the new software for course assessments. Marks were allocated to each practicalskill identified in completing the assessments. Students’ grade attainments of the two course outcomes mapped to the psychomotor domain show that more than 85% of students were able to achieve the KPI of 50% for the two course outcomes. This shows that most students were able to grasp the relevant practical skills required in the course when taught via ODL.