Deepak Kulyal,
V.K. Verma,
Aman Kumar,
- , Research Scholar, Department of Civil Engineering, College of Technology, G.B.P.U.A.T., Pantnagar, Uttarakhand, India, ,
- , Associate Professor, Department of Civil Engineering, College of Technology, G.B.P.U.A.T., Pantnagar, Uttarakhand, India, ,
- , M.Tech. Student, Department of Civil Engineering, College of Technology, G.B.P.U.A.T., Pantnagar, Uttarakhand, India, ,
Abstract
Polyhouses are specialised agricultural structures developed to maintain controlled environmental conditions for crop cultivation, thereby ensuring consistent productivity even under adverse climatic circumstances. The performance of these systems largely relies on the structural stability and cost efficiency of the roof truss, which must achieve an effective balance between strength, adaptability, and economy. In this research, an Artificial Neural Network (ANN)-based modelling framework is introduced to optimise the members of polyhouse roof trusses, offering a computationally intelligent solution aligned with current advancements in innovative materials and infrastructure technologies. A total of 340 truss models, incorporating different load combinations, geometric parameters, and purlin spacings, were evaluated using STAAD.PRO software. The generated dataset was subsequently used to train and validate the ANN model for predicting the most economical pipe cross-sections of truss members under varying loading conditions. The results indicate that the ANN-based approach considerably reduces prediction errors and computational effort compared with traditional design methodologies. Overall, the proposed framework provides a structured, data-oriented strategy for optimising polyhouse roof truss systems. This method improves design precision and efficiency while supporting sustainable and technologically progressive construction practices in agricultural infrastructure.
Keywords: Artificial neural network (ANN), STAAD.PRO, polyhouse, truss, strength
[This article belongs to Recent Trends in Civil Engineering & Technology ]
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Recent Trends in Civil Engineering & Technology
| Volume | 16 | |
| Issue | 02 | |
| Received | 01/04/2026 | |
| Accepted | 25/05/2026 | |
| Published | 29/05/2026 | |
| Publication Time | 58 Days |