Taking advantage of the strong adsorption characteristics of coffee grounds (CGs) and coffee ground biochar (CGB), this research employed equal amounts of 2%, 4%, 6%, and 8% CGs and CGB to replace cement. This study thereby examined the impacts of CGs and CGB on cement compressive strength, as well as their abilities to adsorb chloride ions and formaldehyde. X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TG−DTG), scanning electron microscopy (SEM), and X−ray photoelectron spectroscopy (XPS) were employed to investigate the hydration mechanism and characterize the microscopic structure. The results show the following: (1) The presence of a substantial quantity of organic compounds in CGs is found to have an adverse effect on both the compressive strength and hydration degree of the sample. The use of CGB after high-temperature pyrolysis of phosphoric acid can effectively improve the negative impact of organic compounds on the sample. (2) The addition of CGs reduces the adsorption of chloride ions by cement, primarily due to the presence of fewer hydration products. However, when CGB was incorporated into cement, it enhanced the ability to adsorb chloride ions. (3) Cement containing 8% CGB content can slightly enhance the adsorption of formaldehyde. However, the cement sample with 8% CGB content exhibited the most significant ability to adsorb formaldehyde.

An inorganic polymer compound was used to coat lightweight expanded clay aggregates (ceramsites) with a thin layer. These modified aggregates were then used to manufacture modified ceramsite concrete specimens. The spalling and mechanical properties, including cube and cylinder compressive strengths, tensile strength, flexural strength, elastic modulus and stress–strain relationship, of the concrete specimens at room temperature and after exposure to high temperatures (200–1200°C) were tested. Furthermore, chemical changes of the inorganic polymer compound and macro- and microstructures of the concrete were also detected after exposure to the different temperatures by means of Fourier transform infrared spectroscopy, charge-coupled device high-temperature in situ digital image collection and scanning electron microscopy, respectively. Normal ceramsite concrete and crushed limestone concrete were also produced as references for comparison. It was found that only 4·2% of the modified ceramsite concrete specimens spalled, while 69·4% of the normal ceramsite concrete specimens and 33·3% of the crushed limestone concrete suffered spalling. After exposure to 1200°C, modified ceramsite concrete still had considerable residual mechanical properties. The polymer used as the modification material decomposed gradually with increasing temperature, which could generate channels for vapour release to reduce the possibility of concrete spalling.

In this paper, the thermal-related stress–strain behavior of alkali-activated slag (AAS) concretes, with different alkali concentrations and moduli, was studied under compression. After exposure to high temperatures (200 °C, 400 °C, 600 °C, 800 °C, and 1000 °C), a compression test was carried out on the specimens. The stress–strain relationship, axial compressive strength, and elastic modulus were expressed using both a displacement extensometer and the digital image correlation (DIC) technique. It was mainly determined that: (1) With the increase in temperature, the stress–strain curves of the AAS concretes tended to be flattened, indicating reductions in both axial compressive strength and elastic modulus. After 1000 °C, only 2.5–3.7% axial compressive strength and 1.4–3.9% elastic modulus remained, respectively. (2) The DIC technique was used for thermal strain measurements of the AAS concrete. Compared to the traditional extensometer, DIC yielded a small error of 4.5% and 7.2% for axial compressive strength and elastic modulus measurements, respectively. The strain cloud chart obtained from DIC was helpful for monitoring the damage process of the specimens. The findings of this paper refined scientific systems of AAS concrete under thermal action, and also provided a newly non-contact approach for thermal strain measurements of AAS concrete under compression.

Compressive strength of alkali‐activated slag (AAS) concrete is influenced by multi‐factors in a nonlinear way. Both artificial neural network (ANN) and alternating conditional expectation (ACE) models of 3‐day (3 d) and 28‐day (28 d) compressive strength of AAS were established in this study by using the data reported in related literature, where alkali concentration of activator (Na₂O%), modulus of activator (Ms), water/binder ratio (W/B), surface area of slag (SA), and basicity index of slag (K_b) were taken as input parameters. The models were employed later to predict 3 d and 28 d compressive strength of AAS concretes, respectively, and the results were validated by experimental work. The results show that both the ANN and the ACE models had adequate accuracy, no matter 3 d or 28 d compressive strength was considered. Compared to the 3 d compressive strength, due to data scattering that increased with the increase of data size, both the models did not yield a higher accuracy in the case of 28 d strength. However, also due to the increase in data size, both the models were more feasible to implement 28 d strength prediction as a result of sufficient learning and training during modeling. In addition, based on ACE analysis, the weight‐influencing compressive strength of AAS decreased in a sequence of Na₂O% > Ms > W/B > K_b > SA. If data size was sufficiently large, it was more suitable to establish an ANN model for compressive strength prediction of AAS concretes. Otherwise, ACE could be considered as an alternative to yield an acceptable result.

In recent years, there has been an increasing number of fires in buildings. The methods for detecting residual properties of buildings after fires are commonly destructive and subjective. In this context, property prediction based on mathematical modeling has exhibited its potential. Backpropagation (BP), particle swarm algorithms optimized-BP (PSO-BP) and random forest (RF) models were established in this paper using 1803 sets of data from the literature. Material and relevant heating parameters, as well as compressive strength loss percentage, were used as input and output parameters, respectively. Experimental work was also carried out to evaluate the feasibility of the models for prediction. The accuracy of all the models was sufficiently high, and they were also much more feasible for prediction. Moreover, based on the RF model, the importance of the inputting parameters was ranked as well. Such prediction has provided a new perspective to non-destructively and objectively assess the post-fire properties of concrete. Additionally, this model could be used to guide performance-based design for fire-resistant concrete.