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Department of Engineering Structures
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total: 5
Open Research Data
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Elastic wave propagation signals in concrete cube (experimental and calculated using discrete element method)
Open Research DataThe DataSet contains the results of the elastic wave propagation. Both experimental and numerical signals were obtained for the concrete cube with dimensions of 50 × 50 × 50 mm3. The specimen was made of concrete with called mortar concrete. The ingredients of the concrete mix were as follows: CEM I 42.5R (500 kg/m3), sand 0 – 2 (1500 kg/m3) and water...
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Ambient vibrations - footbridge over the Kolibkowski Stream in Gdynia, span P2
Open Research DataAmbient vibration tests carried out on the P2 span of the footbridge over the Kolibkowski Stream. The research was carried out using a set of 14 acceleration sensors - MEMS accelerometers TE 4332M3-002 and TE 4312M3-002, which allowed for simultaneous measurement and recording of 20 acceleration channels. These sensors are characterized by a natural...
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Ambient vibrations - footbridge over the Kolibkowski Stream in Gdynia, span P3
Open Research DataAmbient vibration tests carried out on the P3 span of the footbridge over the Kolibkowski Stream. The research was carried out using a set of 14 acceleration sensors - MEMS accelerometers TE 4332M3-002 and TE 4312M3-002, which allowed for simultaneous measurement and recording of 20 acceleration channels. These sensors are characterized by a natural...
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Acoustic emission signals in concrete beams under 3-point bending (beams #1, #2, #3)
Open Research DataThe DataSet contains the results of the mechanical behaviour of concrete beams with dimensions 40 x 40 x 160 cm3under the 3-point bending. The beams were made of concrete with the following ingredients: cement CEM I 42.5R (330 kg/m3), aggregate 0/2 mm (710 kg/m3), aggregate 2/8 mm (664 kg/m3), aggregate 8/16 mm (500 kg/m3), water (165 kg/m3) and super-plasticizer...
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Source code - AI models (MLM1-5 - series I-III - QNM opt)
Open Research DataSource code - AI models (MLM1-5 - series I-III - QNM opt) for the paper "Computational Complexity and Its Influence on Concrete Compressive Strength Prediction Capabilities of Machine Learning Models for Concrete Mix Design Support" accepted for publication.