A Hybrid Deep Learning Framework for the AI-Assisted Design of Culturally Sustainable Sculptures

Rafat Saleh Madani

doi:10.48084/etasr.14808

Authors

Rafat Saleh Madani Product Design Department, College of Designs and Arts, Umm Al-Qura University, Makkah, Saudi Arabia

Volume: 15 | Issue: 6 | Pages: 29999-30006 | December 2025 | https://doi.org/10.48084/etasr.14808

Received: 15 September 2025 | Revised: 7 October 2025 | Accepted: 13 October 2025 | Online: 8 December 2025

Corresponding author: Rafat Saleh Madani

Abstract

The integration of Artificial Intelligence (AI) into creative and cultural fields marks a significant technological shift, offering new ways to enhance creativity by combining traditional techniques with data-driven insights. This study addresses a specific need in Jeddah, Saudi Arabia, to harmonize urban development with cultural preservation, a key objective of Saudi Arabia's Vision 2030. It highlights a research gap at the intersection of AI and cultural sustainability, particularly in the design of public art that reflects local identity. To address this issue, the paper introduced a new four-phase hybrid deep learning framework for the AI-assisted design of culturally sustainable sculptures. The approach involves building a curated dataset of Jeddah's cultural aesthetics, using unsupervised learning to group artistic styles and supervised learning to assess public sentiment. These insights are integrated into a practical AI tool for artists. The framework's effectiveness was illustrated through a case study on the Jeddah Corniche, where it helped guide the iterative design of a sculpture, increasing its predicted public approval from 62% to 89%. This work introduces a new AI model for cultural style and sentiment analysis, providing a scalable, evidence-based approach to foster cultural sustainability in urban development through technology.

Keywords:

artificial intelligence, deep learning, Saudi cultural, sculptures, cultural sustainability

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