Deepak Kulyal,
V.K. Verma,
Aman Kumar,
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 |