Alex Hall

Startup Concrete.ai says field tests show its technology reduced CO2 emissions by 30%, while cutting costs by more than $5 per cubic yard.

With the coronavirus pandemic worsening drastically this summer, executives at Suffolk Construction, an AGC of New York State LLC and Rhode Island Chapter-AGC member, figure it’s only a matter of time before a worker shows up sick to one of its many jobsites across the country. But for COVID-19 to spread at one of those sites, it will have to jump through more hoops than a circus tiger.

Testing is “not a solution you can do for every employee, every day”.

Companies are increasingly turning to tech to help mitigate the spread of COVID-19 among their workers. Individually, these technologies provide limited help, but together they may keep workers vigilant about their health.

https://www.nytimes.com/2020/04/28/nyregion/nyc-construction-coronavirus-safety.html

More American workers are testing positive for marijuana, a new report finds, as lawmakers in New Jersey and Illinois push to join nearly a dozen more states where recreational use of the drug is now legal.

Construction companies can’t afford to scrimp on jobsite safety because even a single accident comes at too high a price, say industry professionals and insurers.

However, the growing availability of safety wearables, site sensors, exoskeletons, image-generating drones and other safety-oriented systems raises a key question: Are the results they provide worth the costs, particularly among contractors with limited dollars at their disposal?

The use of statistical and machine learning approaches to predict the compressive strength of concrete based on mixture proportions, on account of its industrial importance, has received significant attention. However, previous studies have been limited to small, laboratory-produced data sets. This study presents the first analysis of a large data set (> 10,000 observations) of measured compressive strengths from actual (job-site) mixtures and their corresponding actual mixture proportions… Show more

The use of statistical and machine learning approaches to predict the compressive strength of concrete based on mixture proportions, on account of its industrial importance, has received significant attention. However, previous studies have been limited to small, laboratory-produced data sets. This study presents the first analysis of a large data set (> 10,000 observations) of measured compressive strengths from actual (job-site) mixtures and their corresponding actual mixture proportions. Predictive models are applied to examine relationships between the mixture design variables and strength, and to thereby develop an estimate of the (28-day) strength. These models are also applied to a laboratory-based data set of strength measurements published by Yeh et al. (1998) and the performance of the models across both data sets is compared. Furthermore, to illustrate the value of such
models beyond simply strength prediction, they are used to design optimal concrete mixtures that minimize cost and embodied CO2 impact while satisfying imposed target strengths. Show less