Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water–cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC’s strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R2), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R2 of 0.91 and 0.86 for compressive and flexural strength, respectively. The models’ decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.

Pure mode I and mode II fracture toughness of asphalt mixture was investigated both experimentally and theoretically using vertical edge crack semicircular bend (SCB) specimen with symmetric and asymmetric bottom loading spans. The thickness of SCB specimen was considered in the range from 30 to 70 mm, and the fracture toughness tests were performed at three subzero temperatures (−5 C, −15 C and −25 C). The results showed that K Ic and K IIc are increased by reducing the test temperature. The thickness had no sound effect on the fracture toughness, but the thickness of 50 mm was more suitable for fracture study on this material. The fracture load of mode II specimen was significantly greater than the mode I fracture load at the same testing condition. The fracture toughness ratio (K IIc /K Ic) that varies in the range between 1 and 1.4 was predicted theoretically using three well-known stress/strainbased fracture criteria. K E Y W O R D S asphalt mixture, fracture toughness ratio prediction, K Ic and K IIc value, low temperature, symmetric and asymmetric SCB specimens, thickness effect

In this Letter, a polarimetric analyzer is designed for a mid-wave infrared camera. This kind of infrared camera transforms into the mid-wave infrared polarization imaging system to measure the infrared polarization characteristics of the object in the moving scene. The polarimetric analyzer is designed by using the ultra-high-speed and high-position method to drive the polarizer to rotate uniformly at the speed of 900 rpm. The polarization state of the object scene is changed, and the mid-wave infrared camera synchronously acquires the infrared intensity image in different polarized directions, those of 0°, 120°, and 240°. Then, a Stokes vector model is established with the basic rotation angles, and a sort-iteration method is proposed to process the original infrared intensity image. Three continuously neighboring infrared intensity images are used to calculate the degree of linear polarization (DoLP) and the angle of polarization (AoP), which make the infrared polarization image the same imaging frame as the infrared intensity image. Test results show that the mid-wave infrared polarization imaging system can complete the acquisition of the DoLP and the AoP images well with the frame frequency of 45 fps, which is suitable for the infrared polarization detection of the moving scenes. The study has great potential for polarization remote sensing and marine object detection.

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Through the foam concrete block cement type, water reducer type, content and tailings using water reducing powder content of the different experimental groups, analyzed the relationship between raw material composition and amount due to compressive strength of foam concrete and different effects of moisture content and compressive strength, and thus presumably the ideal mix. It can be used for future theoretical study reference.