{"id":30874,"date":"2025-06-12T06:13:27","date_gmt":"2025-06-12T13:13:27","guid":{"rendered":"https:\/\/essential.construction\/news\/ai-helps-deliver-better-concrete-more-efficiently\/"},"modified":"2025-06-12T06:13:27","modified_gmt":"2025-06-12T13:13:27","slug":"ai-helps-deliver-better-concrete-more-efficiently","status":"publish","type":"post","link":"https:\/\/aec-business.com\/ai-helps-deliver-better-concrete-more-efficiently\/","title":{"rendered":"AI Helps Deliver Better Concrete More Efficiently"},"content":{"rendered":"

\"-\"<\/a>
\n<\/p>\n

\n

The
\nconstruction industry uses 13 billion cubic meters of ready-mix concrete every
\nyear. The logistics and quality assurance of this concrete depend on legacy
\ntechnologies that are no longer sufficient for today\u2019s needs. To address this, Caidio
\nis introducing AI-based solutions to help the supply chain deliver better
\nquality concrete more efficiently.<\/p>\n

<\/p>\n

The Challenges of the
\nConcrete Supply Chain<\/h2>\n

The ready-mix
\nconcrete supply chain process starts at the batch plant that produces the
\nconcrete (typically a wet mix in Finland). Transit mixers then deliver the
\nconcrete to the construction site. At the site, the concrete is poured or
\npumped into molds and left to cure and dry. What happens at each stage has an
\neffect on the quality of the final product.<\/p>\n

\"-\"\"-\"
The ready-mix concrete process (Image: Caidio)<\/figcaption><\/figure>\n

Many of the
\nmeasurement methods used in concrete quality assurance date back to the 1970s
\nor even earlier. Documentation is usually paper based, and phone calls remain
\nthe main communication channel. Outdated practices make it hard to control the
\nquality of the product throughout the process.<\/p>\n

This is
\nbecoming increasingly apparent as demonstrated by some recent worrying examples
\nof concrete quality problems in Finland. For example, in Northern Finland, in
\nthe city of Kemij\u00e4rvi, a newly built bridge had to be dismantled because of
\nfragile concrete. In Turku, concrete strength deficiencies halted the
\nconstruction of a large hospital extension; indeed, the problems were so
\nserious that they added an extra year to the construction schedule.<\/p>\n

Collaborating to Make the
\nIndustry Better<\/h2>\n
\n
\"-\"\"-\"
Aku Wilenius, CEO of Caidio<\/figcaption><\/figure>\n<\/div>\n

A group of
\nconstruction industry forerunners decided to get together to attempt to solve
\nthe quality problems. In late 2017, the newly established DigiConcrete<\/a> working group arranged a workshop to identify the challenges in concrete
\nconstruction. The group believed that digitalization was the key to better
\nquality.<\/p>\n

The next
\nspring, the group identified Artificial Intelligence (AI) and the Internet of
\nThings (IoT) as potential solutions to the problems. Unfortunately, DigiConcrete
\ncould not find any companies offering AI solutions for the concrete value
\nchain. That discovery led to the establishment of Caidio<\/a>, a concrete intelligence
\nstartup.<\/p>\n

\u201cPasi Karppinen,
\nwhom I already knew and whose firm was a member of the group, suggested that we
\nshould start up a company, one that would revolutionize the industry,\u201d says Aku Wilenius<\/strong>, a former National
\nInstruments engineer, who joined the team and became the CEO of that startup.<\/p>\n

Experimenting with AI<\/h2>\n

Working with members of the DigiConcrete group, Caidio started an experimentation project to study and test the possibilities of using AI for concrete construction. The DigiConcrete project received funding from the Finnish government\u2019s KIRA-digi<\/a> program.<\/p>\n

One of
\nproject partners was Congrid, a company offering a digital alternative to paper-based
\nquality reporting. In the experiments, Caidio used Congrid\u2019s mobile concrete
\nlog sheet to test how AI could be used to analyze it.<\/p>\n

\u201cAnother
\ntest involved an AI-based construction assistant. As we know, knowledge in the
\nindustry is very much person-specific and we wondered how that knowledge could
\nbe passed on to a less-experienced worker,\u201d Wilenius explains.<\/p>\n

The idea
\nwas that the digital assistant could collect data across the entire concrete
\nconstruction process, including from design models and databases, to open data
\nsources, and IoT sensors. It could use weather and traffic data to determine
\nthe behavior of ready-mix concrete during transport. It was intended that AI algorithms
\nwould control the whole process and product quality, prompting users to make the
\nright decisions throughout the process.<\/p>\n

One of the
\nexperiments tested locational tags to track down concrete pumps on a
\nconstruction site. Surprisingly, it\u2019s not always straightforward for a truck
\ndriver to find the right pump on a large site.<\/p>\n

The
\nKIRA-digi experiment also included tests on concrete quantity estimation, mold
\nheating during the winter, logistics optimization, raw material quality control,
\nand safety on the construction site. In addition, Aalto University is developing
\nnew digital measurement methods and sensor technology for concrete quality
\nmeasurement in collaboration with the DigiConcrete project.<\/p>\n

\u201cIn 2019,
\nwe\u2019re planning to pick the most feasible use cases from our experiments and to start
\npiloting them in real-life construction projects,\u201d says Wilenius. \u201cAs a
\ncompany, we\u2019ll focus on providing the underlying intelligence for all relevant processes.
\nWe\u2019re also always happy to collaborate with others who want to develop apps and
\nuser interfaces that utilize our inference machine.\u201d<\/p>\n

You can
\nconnect with Aku Wilenius on LinkedIn.<\/a><\/p>\n<\/div>\n