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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

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In recent years, the development of computer network technology, especially the hot Internet of things, has brought great convenience and comfort to people's life. Steel structure is also more and more widely used in the field of construction in China. The purpose of this study is to explore the application of Internet of things health monitoring system in steel structure construction management. In this study, through the use of example analysis, comprehensive data analysis and other methods, combined with steel structure health monitoring system and construction process management analysis. The results show that the relative deviation of the data within 1-33 is mostly less than 10%, but the relative deviation of 34-40 suddenly increases to 60%, and the actual data suddenly decreases, resulting in the deviation of about 50%. It is concluded that the time series model established in this study can objectively reflect the change trend and law of strain data, has certain prediction accuracy, and has certain value significance for monitoring data analysis and construction management of steel building structures.

This paper presents an experimental study on use of hot-melt polyamide (HMP) to prepare mortar specimens with improved crack healing and engineering properties. The role of HMP in the crack repairing of cement mortar subjected to several rounds of heat treatment was investigated. Compatibility between HMP and hydraulic cement was investigated through X-ray diffraction (XRD) and Fourier transform infrared spectra (FTIR) technology. Mortar specimens were prepared using standard cement mortar mixes with HMP at 1%, 3% and 5% (by volume) for fine aggregate substitute. After curing for 28 days, HMP specimens were subjected to heating at temperature of 160 °C for one, two, and three days and then natural cooling down to ambient temperature. Mechanical and durability properties of the heated HMP mortars were evaluated and compared with those of the corresponding mortars without heating. The microscopic observation of the interfacial transition zone (ITZ) of HMP mortar was conducted through environmental scanning electron microscopy (ESEM). Results reveal that incorporation of HMP improves the workability of the HMP/cement binder while leading to decrease in compressive strength and durability. The heated HMP mortars after exposure to heating for one, two, and three days exhibit no obvious change in compressive strength while presenting notable increase in flexural strength and durability compared with the corresponding mortars without heating. The XRD, FTIR and ESEM analyses indicate that no obvious chemical reaction occurs between HMP and hydraulic cement, and thus the self-repairing for interfacial micro-crack in HMP/cement composite system is ascribed to the physical adhesion of HMP to cement matrix rather than the chemical bonding between them.

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An experimental program was carried out to investigate whether EVA (ethylene vinyl acetate copolymer) heat-melt adhesive can potentially act as a self-healing agent in cement-based material. The effects of incorporation of EVA and heating on the properties of mortar were studied. Self-healing capacity of EVA specimens was also verifi ed. The experimental results show that the addition of EVA would not greatly affect original characteristics of the matrix when EVA content was less than 5%; the interface between EVA and cement matrix was well improved after heating, which allows a signifi cant improvement in fl exural strength and toughness of specimen; pre-damaged specimens in various degrees (30%, 50% and 70%) were effectively repaired by EVA and the repair effi ciency all exceeded 100%.