Abstract: A method and a device for controlling the delivery rate of a concrete pump for filling a formwork arrangement with a pumpable filling compound as a function of curing-relevant material and environmental parameters, after which the installed filling compound sets within the formwork arrangement, comprising the following steps: determining a permissible climbing speed for filling the formwork arrangement with the filling compound on the basis of the material and ambient parameters, measuring the static filling compound pressure acting on the formwork arrangement during filling, and calculating the permissible delivery rate of the concrete pump as a function of the determined permissible rate of ascent and the measured static filling compound pressure at the formwork arrangement.

Abstract: The invention relates to a method and a device for controlling the delivery rate (PQ) of a concrete pump (1) for filling a formwork arrangement (2) with a pumpable filling compound (3) as a function of curing-relevant material and environmental parameters (RST), after which the installed filling compound (3) sets within the formwork arrangement (2), comprising the following steps: determining a permissible climbing speed (vsz) for filling the formwork arrangement (2) with the filling compound (3) on the basis of the material and ambient parameters (RST), measuring the static filling compound pressure (pF) acting on the formwork arrangement (2) during filling, calculating the permissible delivery rate (PQ) of the concrete pump (1) as a function of the determined permissible rate of ascent (vsz) and the measured static filling compound pressure (pF) at the formwork arrangement (2).

Abstract: Method (100) for training an artificial neural network, KNN (1), which predicts and/or classifies at least one quality measure (23a, 23b) for the usability of a batch of concrete (2) on a construction site. A method (200) for predicting and/or classifying the usability of a batch of concrete (2) on a construction site, comprising the steps of: a set of characteristics (21) characterising the material composition of the batch (2) is determined (210); at least one measure (22) of the mechanical consistency of the batch (2) is determined (220); the characteristics (21) and the measure (22) of mechanical consistency are fed (230) to a trained KNN (1) as inputs (11); at least one prediction and/or classification (23a*, 23b*) for a quality measure (23a, 23b) for the usability of the batch (2) for at least one intended use on the construction site is retrieved (240) as an output (13) from the KNN (1).