Artificial Neural Network-Based Normal Strength Prediction of Coir Fiber Concrete Hollow Blocks with Rice Husk Ash
2026 9th International Conference on Artificial Intelligence and Big Data (ICAIBD), (2026), pp. 75-80
Rizza P. Gamalinda
a
,
Dante L. Silva
b
,
Kevin Lawrence M. De Jesus
c
,
Jimmy G. Catanes
d
,
Nenita B. Nagarit
e
,
Meriam P. Leopoldo
f
,
Crispin S. Lictaoa
g
,
Godofredo Mendoza
h
a School of Graduate Studies, Mapúa University, Manila, Philippines
b School of Civil, Environmental and Geological Engineering Mapúa University, Manila, Philippines
c Department of Civil Engineering, FEU Institute of Technology, Manila, Philippines
d Commission on Higher Education, Philippines, Manila, Philippines
e Philippine Association of Colleges and University, Commission on Accreditation, Manila, Philippines
f College of Engineering and Architecture, Mapúa Malayan Colleges Mindanao, Davao City, Philippines
g Civil Engineering Department, Adamson University, Manila, Philippines
h College of Engineering National University, Philippines, Manila, Philippines
Abstract: Virtual laboratories based on Building Information Modeling (BIM) are increasingly used in civil engineering (CE) education to connect theoretical instruction with industryoriented digital practice. However, limited empirical evidence identifies and prioritizes the factors influencing their effectiveness, particularly from the perspective of engineering educators. This study evaluates the effectiveness of BIM-based virtual laboratories in the Bachelor of Science in Civil Engineering program using Artificial Neural Network (ANN) modeling and sensitivity analysis. Data gathered from engineereducators were grouped into five categories: technological, pedagogical, learner-related, environmental and institutional, and outcome-related factors. The ANN model mapped these factors to perceived overall effectiveness, while sensitivity analysis quantified their relative importance. Results indicated that system usability was the most influential factor, followed by integration with coursework, instructor expertise, system performance and reliability, and student self-efficacy. These findings provide evidence-based guidance for improving technological design, pedagogical alignment, faculty capacity, and sustainable implementation of BIM-enabled virtual laboratories in CE education.