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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-archives-XLIX-B4-2026-471-2026</article-id>
<title-group>
<article-title>Developing BIM-Based Data Analytics Dashboards for Sustainable Construction and Demolition Waste Management and Environmental Evaluation</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Abbasian</surname>
<given-names>Sara</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Jadidi</surname>
<given-names>Mojgan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Civil Engineering Department, Lassonde School of Engineering, York University, 4700 Keele St, Toronto, Canada</addr-line>
</aff>
<pub-date pub-type="epub">
<day>04</day>
<month>08</month>
<year>2026</year>
</pub-date>
<volume>XLIX-B4-2026</volume>
<fpage>471</fpage>
<lpage>476</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Sara Abbasian</copyright-statement>
<copyright-year>2026</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/471/2026/isprs-archives-XLIX-B4-2026-471-2026.html">This article is available from https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/471/2026/isprs-archives-XLIX-B4-2026-471-2026.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/471/2026/isprs-archives-XLIX-B4-2026-471-2026.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLIX-B4-2026/471/2026/isprs-archives-XLIX-B4-2026-471-2026.pdf</self-uri>
<abstract>
<p>Building Information Modelling (BIM) is increasingly mandated worldwide as part of the construction industry&apos;s digital transformation. While widely used in design and construction, its potential for managing construction and demolition waste (C&amp;amp;DW) remains underexplored, despite demolition accounting for 70&amp;ndash;90% of building-related waste and 30&amp;ndash;40% of global solid waste. BIM Revit native models provide rich data but are computationally intensive and require specialist expertise, limiting their direct use for waste quantification and sustainability evaluation. This paper develops a BIM-enabled data integration and visualization framework that automates waste estimation, material classification, and environmental evaluation by linking BIM data with heterogeneous datasets through Speckle connectors and Power BI dashboards. Supplementary datasets included material densities, expansion coefficients, recycling rates, and environmental factors such as CO&lt;sub&gt;2&lt;/sub&gt; emissions and energy intensities. A case study of York University&amp;rsquo;s Bergeron Centre illustrates the framework&amp;rsquo;s effectiveness across three demolition stages. The non-invasive dismantling phase highlighted significant opportunities for material recovery, while semi-invasive deconstruction captured recyclable structural components with moderate landfill requirements. The final core demolition stage revealed the greatest potential for recycling, particularly in concrete and steel, though it also underscored the challenges of diverting large volumes of residual waste from disposal. By integrating BIM with environmental datasets and interactive dashboards, the system delivered holistic insights into recovery, landfill diversion, and CO&lt;sub&gt;2&lt;/sub&gt; reduction. Findings confirm its scalability, accessibility, and value as a decision-support tool for sustainable demolition and circular economy objectives.</p>
</abstract>
<counts><page-count count="6"/></counts>
</article-meta>
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