This study investigates the use of wood ash (WA3 and WA4), both unground and ground for 10 and 20 min, as a partial replacement for Portland cement in concrete. Fly ash (FA) is used as a reference. The effects of incorporating ground and unground wood ash on fresh and hardened properties, hydration behavior, microstructure, durability, and environmental performance were evaluated. Grinding wood ash significantly improved the compressive strength of concrete and increased the cumulative heat release during hydration. Air-entraining admixtures significantly improved the freeze-thaw resistance of concrete with ground wood ash and reduced surface scaling compared to unground ash. Furthermore, grinding reduced the mobility of key heavy metals, enhancing environmental compatibility. Overall, 10-minute grinding provided the optimal balance between performance and environmental safety. These results support the controlled use of ground wood ash as a sustainable supplementary cementitious material in concrete, contributing to both concrete performance and environmental benefits.

Wood ash is a promising supplementary cementitious material (SCM) due to its inherent pozzolanic properties. Intensive grinding has been shown to enhance this aspect and reduce the negative effects of variability in the chemical composition. This study investigated the influence of grinding through ball milling on the pozzolanic properties of wood ash. Four different types of wood ash were studied, each subjected to grinding durations of 10 and 20 min. Coal fly ash was used as a reference material. The pozzolanic activity of raw and ground wood ashes was evaluated using the strength activity index (SAI), the Frattini test, the R3 test, thermogravimetric analysis (TGA/DTG), X-ray diffraction (XRD) analysis, and scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS). The results indicated that both 10 min and 20 min grinding durations enhanced the reactivity and compressive strength. However, the 10 min grinding duration showed better overall performance than 20 min grinding, likely due to reduced agglomeration and more effective particle refinement. For calcium-rich wood ashes, the reactivity was linked to the hydraulic properties rather than the pozzolanic properties.

Mortar beams incorporating carbon nanofibers (CNFs), which were synthesized in situ on Portland cement particles, were used to produce nanomodified Portland cement sensors (SmartCem sensors). SmartCem sensors exhibited an electrical response comparable to a thermistor with a temperature coefficient of resistivity of − 0.0152/ °C. The highest temperature sensing was obtained for the SmartCem sensor, which contained ~ 0.271 wt.% of CNFs. The calculated temperature sensitivity was approximately 11.76% higher in comparison with the mortar beam containing only unmodified Portland cement. SmartCem sensors were used to monitor the cement hydration in large-scale self-compacting concrete beams. The measurements were conducted after casting for 7 days. Additionally, commercially available thermocouple and humidity sensors were used as references. The results showed that changes in electrical resistivity measured by the SmartCem sensor were well aligned with the ongoing hydration processes.

Mortar sensors were fabricated as beams incorporating different amounts of carbon nanofibers (CNFs) synthesized in-situ on cement particles. Changes in electrical resistivity were measured and compared to recorded changes in compressive stress, temperature, and humidity. Sensing mechanisms and corresponding models were developed. The findings of the study indicate that the piezoresistive effect is influenced by the critical concentration of CNFs inside the composite matrix and the tunneling effect. In addition, water absorption and desorption, as well as the amount of chemically bound water played an important role in humidity sensing. Thermal fluctuation-induced tunneling conduction was dominant for the temperature sensitivity.

Concrete's ability to auto-repair the cracks reduces the need for maintenance and repair. Autogenous self-healing is an intrinsic property of concrete highly dependent on the binder composition. The urgent necessity to decrease CO2 emissions of concrete by replacing cement with “greener” materials provides challenges and opportunities for self-healing cementitious materials. This research aims to verify the self-healing behavior of environmentally friendly multi-component binders. An experimental study is conducted to test the effect of binder composition-related parameters (e.g., phase composition, porosity) and crack geometry on the self-healing efficiency of the “green” mortars. Cementitious materials with 50 wt.%cement replacement with limestone powder blended with fly ash, blast furnace slag, and silica fume are investigated. Sorptivity change, compressive strength regains, and crack closure after self-healing are used to quantify the self-healing efficiency. Quantitative analysis and correlations between chemical composition/microstructural features, geometrical crack characteristics, and self-healing measures are investigated. The results indicate that “green” binder composition affects the self-healing mechanism leading to different levels of performance recovery. Some SCMs-limestone binder formulations enable a better self-healing efficiency than pure OPC or OPC/limestone cementitious materials, presumably due to a synergistic effect between the limestone and the mineral additions. Correlation analysis indicated that geometrical complexity characterized by fractal dimension and tortuosity of the crack does not affect the external crack closure, whereas the fractal dimension and maximum crack width are correlated with the internal crack healing.

Autogenous self-healing of post-fire damaged concrete enables structure performance auto-recovery leading to reduced repair costs, less generated waste, and lower CO2 emissions. In this paper, to improve the efficiency of this process and understand the underlying mechanism, the self-healing of 0.1 wt% MWCNT-modified and pure cement paste subjected to novel environmental stimulators was tested. High-temperature damage was induced at 200 °C and 400 °C, followed by a self-healing cyclic treatment with water, a mixture of water with phosphate-based retarding admixture, and limewater. The self-healing efficiency of the proposed solutions were compared based on crack closure, strength regains, porosity, and chemical composition changes. The surface crack closure after 200 °C varied between 33% and 60%, whereas for 400 °C, only retarding admixture exposure obtained over 50% crack closure and the most considerable decrease in average crack width of 33% for MWCNT-modified paste. The highest values of compressive strength recovery, equal to 18% and 14%, exceeding the intact specimen's compressive strength, were observed for the MWCNT-modified paste healed in water and limewater. Water exposure with an extended wetting phase enhanced the compressive strength recovery of the MWCNT-modified materials. Strong (r = 0.87) and moderate (r = 0.52) positive correlations were observed between temperature loading and compressive and flexural strength recovery parameters, respectively. Higher porosity and interconnected crack network, caused by high temperature, facilitated the self-healing process. Porosity changes before and after healing were pronounced in contrast to the amount of unhydrated cement, which did not exhibit noticeable changes. The healing mechanism included three processes: calcite formation, further hydration inside the cracks, and rehydration of the bulk cement paste.Previous article in issue

Developing accurate and interpretable models to forecast concrete’s self-healing behavior is of interest to material engineers, scientists, and civil engineering contractors. Machine learning (ML) and artificial intelligence are powerful tools that allow constructing high-precision predictions, yet often considered “black box” methods due to their complexity. Those approaches are commonly used for the modeling of mechanical properties of concrete with exceptional accuracy; however, there are few studies dealing with the application of ML for the self-healing of cementitious materials. This paper proposes a pioneering study on the utilization of ML for predicting post-fire self-healing of concrete. A large database is constructed based on the literature studies. Twelve input variables are analyzed: w/c, age of concrete, amount of cement, fine aggregate, coarse aggregate, peak loading temperature, duration of peak loading temperature, cooling regime, duration of cooling, curing regime, duration of curing, and specimen volume. The output of the model is the compressive strength recovery, being one of the self-healing efficiency indicators. Four ML methods are optimized and compared based on their performance error: Support Vector Machines (SVM), Regression Trees (RT), Artificial Neural Networks (ANN), and Ensemble of Regression Trees (ET). Monte Carlo analysis is conducted to verify the stability of the selected model. All ML approaches demonstrate satisfying precision, twice as good as linear regression. The ET model is found to be the most optimal with the highest prediction accuracy and sufficient robustness. Model interpretation is performed using Partial Dependence Plots and Individual Conditional Expectation Plots. Temperature, curing regime, and amounts of aggregates are identified as the most significant predictors.

Cement production contributes significantly to carbon dioxide emissions. Alkali-activated materials offer an environmentally friendly alternative due to their comparable strength, durability and low-carbon emissions while utilizing wastes and industrial by-products. Wood ash is a waste material that shows promising results as a partial replacement for Portland cement and precursors in alkali-activated systems. The aim of this study was to examine the effect of ground wood ash on the mechanical properties of alkali-activated mortars. Wood ash was incorporated as a 0 wt%, 10 wt% and 20 wt% partial replacement for ground granulated blast furnace slag (GGBFS). The wood ashes were ground in a planetary ball mill for 10 and 20 min. Sodium silicate (Na2SiO3), sodium carbonate (Na2CO3), and sodium hydroxide (NaOH) were used as alkali activators. The results demonstrated that ground wood ash improved the mechanical properties of alkali-activated systems compared to untreated wood ash. However, the incorporation of wood ash increased the porosity of the binder matrix.

Different ecological binders have been used to minimize the negative effects of cement production and use on the environment. Wood ash is one of these alternative binders, and there has been increasing research related to this topic recently. The wood ash utilized in the literature primarily originates from power plants and local bakeries, and predominantly wood fly ash is used. This review paper examines the use of wood ash as an ecological binder in two different applications: as a cement replacement and as an alkali-activated material. Studies have shown that while increased wood ash content in concrete and mortars can have negative effects on strength and durability, it is still a promising and developable material. Depending on the chemical composition of the wood ash, the strength and durability properties of concrete might be slightly improved by utilizing wood ash as a replacement for cement, with an optimal replacement level of 10–20%. However, there is a need for more research regarding the effects of wood ash on the durability of cement-based materials and its use in alkali-activated materials. Overall, this review provides a comprehensive overview of the properties of wood ash and its potential applications in conventional concrete and mortars, as well as in alkali-activated materials.