Coordinated effects of air pollution control devices on polychlorinated naphthalenes emissions from coal-fired power plants in China.

ABSTRACT With the increase of coal gasification ash production and the environmental problems caused by its accumulation, it is of great significance to study its application in concrete. In this paper, the flexural performance of coal gasification ash concrete beams under low cyclic loading is studied. The effects of coal gasification ash content, concrete strength and reinforcement ratio on the seismic performance of the specimens are discussed. The mechanical properties such as load-deflection, crack width and steel bar strain were measured by experiments. It was found that when the content of coal gasification ash was 20 %, the flexural capacity and seismic performance of the specimen beam were the best. The calculation formulas of cracking load and ultimate bearing capacity based on coal gasification ash were further proposed, and their rationality was verified by ABAQUS finite element simulation. The results show that the proper amount of coal gasification ash can not only improve the mechanical properties of the beam, but also reduce carbon emissions and production costs, which provides a theoretical basis for the promotion of coal gasification ash in engineering applications.

ABSTRACT The physical properties of concrete depend on the type of supplementary cementitious materials (SCMs); thus, its strength must be evaluated for specific purposes. In this study, the Random Forest regression (RFR) machine learning (ML) algorithm was used to estimate the compressive strength (CS) of blended concrete (BC) with fly ash (F_Ash). The hyperparameters of the RFR model were optimized using Particle Swarm Optimization (PSO) to enhance predictive performance. The optimized RFR model served as a surrogate model, and PSO found optimal input parameters for the best response. Key inputs included cement, GGBS, fine aggregate (FA), coarse aggregates (CA), fly ash (F_Ash), water content, superplasticizer and curing days, with CS as the output. Performance evaluation used indices, such as MAE, MAPE, MSE, RMSE, MBE, R 2, a20-index to assess accuracy. Sensitivity analysis showed the relationship between inputs and CS, highlighting the impact of F_Ash and other parameters on CS prediction. The model achieved an RMSE of 2.005 and an R 2 of 0.9858 for CS. The optimized response was confirmed at 71.42 MPa, with optimized input parameters: Cement = 365.94 kg/m3, GGBS = 263.184 kg/m3, F_Ash = 148.610 kg/m3, W = 123.907 litres, SP = 23.58 kg/m3, CA = 820.788 kg/m3, FA = 734.118 kg/m3, and days = 166.710.

ABSTRACT This study develops supervised machine learning models based on least squares support vector machine (LSSVM) and its hybrid variants integrated with particle swarm optimization (PSO), ant colony optimization (ACO) and grey wolf optimization (GWO) to predict CS of red-mud-based concrete. A comprehensive database of 198 experimental datasets including eight input variables and one output variable employed for model development and validation of models. Model performance was evaluated using statistical and error indices. The results indicate that hybrid optimization significantly enhances prediction accuracy, with the PSO-LSSVM model achieving the best performance (R2 = 0.944, RMSE = 0.047, MAE = 0.034), outperforming the standalone LSSVM model (R2 = 0.909, RMSE = 0.058, MAE = 0.045). Sobol sensitivity analysis reveals that fly ash content is the most influential parameter (0.583), followed by water (0.352), superplasticizer (0.277) and red mud (0.259). The findings demonstrate that hybrid LSSVM models provide a reliable and efficient approach for accurate CS prediction and mix optimization in sustainable red-mud-based concrete. The developed models were further integrated into a user-friendly web-based application to facilitate rapid and practical compressive strength prediction for engineers and researchers.

Effects of low pressure and low humidity on hydration and freeze-thaw resistance of air-entrained concrete with fly ash and GGBS

Sustainable resin development for the strategic upcycling of waste fly ash into high-performance polymer composites

Enhancement of fly ash carbonation by mechanical ball milling: From lab-scale characterization to pilot‑scale validation.

From waste to resource: Fly ash regeneration salt recycling based on chlor-alkali production.

Understanding the thermo-mechanical behavior of Low-Cement Concrete (LCC) under fire is essential for its safe and durable use in structures. This study investigates cylindrical specimens (⌀100 × 200 mm) made with Ordinary Portland Cement (OPC)-CS1, 35% Fly Ash (FA)-CS2, and 50% Ground Granulated Blast Furnace Slag (GGBS)-CS3 preloaded to 30% of their 90-day compressive strength and exposed to peak temperatures of 400 °C and 600 °C, followed by cooling to 400 °C, 200 °C or ambient. Axial strain evolution was monitored using Digital Image Correlation (DIC), while residual compressive strength was measured post-exposure. Results show that CS1 exhibited the highest early-age strength and largest thermal expansion, whereas CS2 and CS3 showed slower expansion, earlier onset of contraction, and moderate peak strains. LCC mixes experienced delayed internal heating, reducing thermal gradients during fire exposure. Residual strength was highest for CS1 (77%), followed by CS3 (67%) and CS2 (63%). After heating to 600 °C, concretes lost on average 20-24% of their strength, while 400 °C exposure gave an average loss of 10-15%. Prolonged heating improved SCM concretes’ retention, showing that fly ash and GGBS can limit thermal deformation while maintaining long-term strength and fire resilience. • OPC gains strength rapidly, while LCC develops strength more slowly. • LCC lowers peak expansion and moderates strains under thermal loading. • LCC delays internal heating and reduces thermal gradients during exposure. • Strength loss was 20–24% at 600 °C and 10–15% at 400 °C after cooling. • 50% GGBS concrete shows better post-fire strength than 35% Fly Ash.

Incorporating bio-based residues into cementitious materials offers a promising pathway toward sustainable construction. This study investigates the combined effects of biochar (BC, 0–25%), fly ash (FA), and ladle furnace slag (LFS) on the fresh and hardened properties of cementitious grouts. Twenty grout mixes were designed with Portland cement (PC) replacement levels of up to 60%. Mechanical performance and durability-related properties were evaluated after 28 days of water curing and after 210 days of air curing —under sealed and unsealed conditions. Hydration kinetics and microstructural evolution were assessed using isothermal calorimetry, XRD, and TGA. The results indicate that moderate BC incorporation (≤15%) maintained acceptable workability, particularly when combined with FA. Under air curing, higher BC contents resulted in compressive strength ( f c ) reductions of up to 40% compared to the 100% PC reference. High BC dosage diluted the hydrating matrix and delayed the onset of hydration, reducing silicate reactions and f c . The kinetic differences were also reflected in the XRD data, showing differences in the intensities of the reflections rather than the type of assemblages. Sealed air curing enhanced the f c of all mixes compared to the unsealed condition (25–80%) and reduced water absorption by 15% —especially in ternary systems— by sustaining hydration and mitigating carbonation. Strength Activity Index showed that BC-containing mixes performed relatively better under air curing than water curing, benefiting from the internal curing effect of BC. Flexural strength of BC+LFS mixes reached a comparable value to the reference (≥8 MPa). FA-containing mixes reduced shrinkage (by 22% compared to 100PC) while BC-containing mixes with PC ≥60% showed limited carbonation depth (<5 mm). Overall, optimal performance was achieved in ternary blends with BC contents up to 15%, demonstrating a viable strategy to balance mechanical performance, durability, and sustainability (showing reduced embodied carbon by 68% comparted to the reference) in low-carbon cementitious grouts. • BC incorporation up to 15% in ternary grout systems provided balanced workability, strength, and low embodied carbon. • Sealed curing increased compressive strength by 25–80% compared with unsealed conditions. • BC+LFS ternary blends exhibited higher flexural performance than FA counterparts. • Strength Activity Index indicated improved performance of BC blends under air curing relative to water curing.

Carbon-induced multiscale coupling mechanisms of chlorine release and heavy metal migration during thermal treatment of MSWI fly ash.

Hybrid and explainable machine learning for predicting water penetration depth in seawater based self-compacting concrete

• A sustainable NaOH-activated geopolymer composite was developed using coal fly ash and coconut pith fibers. • The composite exhibited enhanced strength and compactness through optimized NaOH concentration and fiber content. • Advanced characterization (FTIR, XRD, XRF) confirmed the formation of stable aluminosilicate and reinforcing mineral phases. • Machine learning with Bayesian optimization predicted optimal mix parameters, streamlining material design and minimizing resource use. The rapid growth in construction demand has intensified the need for sustainable binders that reduce the environmental burden associated with Portland cement. This study develops a sodium hydroxide (NaOH)-activated coal fly-ash geopolymer composite reinforced with coconut pith, an abundant agricultural by-product, to enhance mechanical performance and material sustainability. A combined experimental and machine learning (ML) framework was implemented to predict and optimize the unconfined compressive strength (UCS) of the composite. Fifteen experimental formulations were used to train ten regression models representing diverse learning biases, and an ensemble surrogate model was subsequently coupled with Bayesian optimization (BO) to identify optimal mix parameters. The BO-predicted formulation (10.8 M NaOH, 3.2 % pith) corresponded closely with the experimentally measured optimum (10 M NaOH, 3 % pith, UCS = 18.32 MPa), confirming the model’s predictive validity. The results demonstrate that integrating ML and BO enables efficient exploration of the compositional space, reducing experimental effort while improving data-driven material design. This hybrid approach establishes a scalable, resource-efficient pathway for developing low-carbon geopolymer binders that valorize agricultural waste and advance sustainable construction technologies.

Characterization of multi-waste concrete incorporating recycled aggregate, asphalt, fly ash, and rubber waste: Structural and environmental assessment

Evaluation of geotextile-based adsorption system in a pilot scale test simulating runoff contaminated with petroleum products and heavy metals.

Valorization of agricultural and industrial wastes in geopolymer foam concrete, a ternary binder approach using corncob ash, red mud, and fly ash

Mix design optimisation for concrete with alternative binders and aggregates incorporating environmental, mechanical and durability performance

Experimental study on the effects of glass hollow microspheres and fly ash on the thermo-mechanical properties, pore structure, and interface characteristics of foamed concrete

Development and performance of ambient cured geopolymer concrete with low alkali activation for sustainable construction

Developing concrete mix designs that enhance structural performance while reducing environmental impact is vital for achieving sustainable construction. This study utilizes multiobjective optimization and machine learning (ML) that aim to reduce the carbon footprint of concrete production by utilizing industrial by-products (e.g., slag and fly ash) as replacements for cement without compromising concrete strength. A data set of 1,105 observations detailing mix compositions, ages, and compressive strengths was utilized to train and test various ML models, including neural networks (NN). The NN model, optimized using grid search, random search, and Bayesian optimization, demonstrated superior performance with an R2 value of 0.89 for compressive strength prediction. The model was further validated using material compositions and compressive strength tests not included in the training or verification data sets. A key innovation of this research lies in applying dimensionality reduction and multiobjective optimization techniques to navigate the trade-off between compressive strength and environmental impact. The Pareto front was generated, highlighting optimal concrete mix designs that achieve compressive strength requirements while reducing carbon emissions by 30%–70%. This design space, derived through ML, enables engineers to make informed decisions about designing sustainable, high-performing concrete mixtures. The findings underscore the drastic potential of ML in advancing sustainable construction practices, achieving structural quality, and mitigating the environmental footprint of building materials.