<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>water-cement ratio &#8211; BIOENGINEER.ORG</title>
	<atom:link href="https://bioengineer.org/tag/water-cement-ratio/feed/" rel="self" type="application/rss+xml" />
	<link>https://bioengineer.org</link>
	<description>Bioengineering</description>
	<lastBuildDate>Sun, 11 Oct 2026 03:55:28 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.1.3</generator>

<image>
	<url>https://bioengineer.org/wp-content/uploads/2019/09/cropped-bioengineering-32x32.png</url>
	<title>water-cement ratio &#8211; BIOENGINEER.ORG</title>
	<link>https://bioengineer.org</link>
	<width>32</width>
	<height>32</height>
</image> 
<site xmlns="com-wordpress:feed-additions:1">72741379</site>	<item>
		<title>AI Cracks the Concrete Code: Explainable Machine Learning Predicts Green Concrete Strength Before Mixing</title>
		<link>https://bioengineer.org/ai-cracks-the-concrete-code-explainable-machine-learning-predicts-green-concrete-strength-before-mixing/</link>
		
		<dc:creator><![CDATA[]]></dc:creator>
		<pubDate>Sun, 11 Oct 2026 03:55:28 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[compressive strength]]></category>
		<category><![CDATA[concrete]]></category>
		<category><![CDATA[construction materials]]></category>
		<category><![CDATA[Explainable AI]]></category>
		<category><![CDATA[fly ash]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[mix design]]></category>
		<category><![CDATA[SHAP]]></category>
		<category><![CDATA[silica fume]]></category>
		<category><![CDATA[supplementary cementitious materials]]></category>
		<category><![CDATA[water-cement ratio]]></category>
		<category><![CDATA[XGBoost]]></category>
		<guid isPermaLink="false">https://bioengineer.org/?p=413934</guid>

					<description><![CDATA[Researchers trained an explainable XGBoost model on 1,670 field-recorded concrete mixes, achieving R-squared 0.9263 in strength prediction while using SHAP analysis to reveal that water-cement ratio, curing time and total cementitious content dominate performance.]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">413934</post-id>	</item>
	</channel>
</rss>
