<p><span style="color: rgba(0, 0, 0, 0.9); font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Ubuntu, Oxygen, Cantarell, 'Fira Sans', 'Droid Sans', 'Helvetica Neue', Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 16px; font-weight: 600; white-space: pre-wrap; background-color: #ffffff;">Generation of Building Plans using ML and AI | </span><span style="background-color: #ffffff; color: rgba(0, 0, 0, 0.9); font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Ubuntu, Oxygen, Cantarell, 'Fira Sans', 'Droid Sans', 'Helvetica Neue', Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; font-size: 16px; font-weight: 600; white-space: pre-wrap;">Springer Nature</span></p>
<p class="MsoNormal"><strong>Technologies for Sustainable Buildings and Infrastructure</strong></p>
<p class="MsoNormal"><strong>Selected Proceedings of SIIOC 2023 |&nbsp;Conference proceedings &copy; 2024</strong></p>
<p data-path-to-node="1">Traditional residential design is often time-consuming and prone to errors. To streamline this process for architects and urban planners, this research explores deep learning models to automate building plan generation.</p>
<p data-path-to-node="2"><strong data-path-to-node="2" data-index-in-node="0">Key Findings:</strong></p>
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<p data-path-to-node="3,0,0"><strong data-path-to-node="3,0,0" data-index-in-node="0">Initial Attempts:</strong> Early experiments using Variational Autoencoders (VAEs) and DALL&middot;E produced pixelated, hard-to-interpret images due to strict constraints.</p>
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<p data-path-to-node="3,1,0"><strong data-path-to-node="3,1,0" data-index-in-node="0">The GAN Solution:</strong> Switching to Generative Adversarial Networks (GANs) significantly improved image clarity and resolution. The generator-discriminator setup requires fewer constraints and produces highly realistic outputs.</p>
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<p data-path-to-node="3,2,0"><strong data-path-to-node="3,2,0" data-index-in-node="0">Current Challenges:</strong> Despite yielding superior results, GANs still face technical hurdles like training instability, mode collapse, and vanishing gradients.</p>
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<p data-path-to-node="4"><strong data-path-to-node="4" data-index-in-node="0">Conclusion:</strong> While further refinement is necessary to resolve training issues, GANs demonstrate strong potential to successfully replace and enhance traditional architectural design methods.</p>