Image analysis techniques are gaining popularity in the studies of civil engineering materials. However, the current established image analysis methods often require advanced machinery and strict image acquisition procedures which may be challenging in actual construction practices. In this study, we develop a simplified image analysis technique that uses images with only a digital camera and does not have a strict image acquisition regime. Mortar with 10%, 20%, 30%, and 40% pozzolanic material as cement replacement are prepared for the study. The properties of mortar are evaluated with flow table test, compressive strength test, water absorption test, and surface porosity based on the proposed image analysis technique. The experimental results show that mortar specimens with 20% processed spent bleaching earth (PSBE) achieve the highest 28-day compressive strength and lowest water absorption. The quantified image analysis results show accurate representation of mortar quality with 20% PSBE mortar having the lowest porosity. The regression analysis found strong correlations between all experimental data and the compressive strength. Hence, the developed technique is verified to be feasible as supplementary mortar properties for the study of mortar with pozzolanic material.

In this study, steel slag (SS) and ground coal bottom ash (GCBA) were utilized to partially substitute for cement in manufacturing ternary blended cement mortar. The replacement ratios of both SS and GCBA ranged from 0% to 20%, and the total replacement ratio varied from 0 to 40%. Response-surface methodology (RSM) and an artificial neural network (ANN) were employed to establish models with which the effects of the various combined contents of SS and GCBA on the distribution of 28-day strength and 91-day strength could be identified. The results showed that the combination of SS and GCBA had a positive effect on strength at a low replacement ratio, while it had an adverse effect on strength at a high replacement ratio. At a late curing age, the pozzolanic reaction of GCBA contributes to the strength enhancement. A total of 15 out of 27 experimental data were used to establish the RSM and ANN models. Through analysis of variance (ANOVA), the models established by RSM were well-fitted with the experimental data. The ANN-trained models also exhibited a good fit with the experimental data, as indicated by an R² of >0.99. The remaining 12 out of 27 experimental data were used for the validation of the developed models, and the performances of the RSM and ANN models in prediction were compared. In conclusion, the ANN showed a better performance in strength prediction.

This study evaluates the effects of steel slag powder (SSP), fly ash (FA), and steel slag sand (SSS) on mortar compressive strength. A response surface methodology (RSM) based on central composite design (CCD) was employed to model 7-day, 28-day, and 91-day strength development, considering three quantitative variables: SSP, FA, and SSS. Statistical results confirmed the reduced cubic models were significant and predictive (R² > 0.97), with non-significant lack of fit and adequate precision. Experimental results revealed that SSP and FA negatively affected early-age strength due to dilution effects and low initial reactivity, whereas SSS slightly improved it by enhancing particle packing. At later ages, SSP exhibited nonlinear effects, where moderate dosages enhanced strength, while excessive replacement led to strength reduction. SSS showed a continuously positive contribution across all ages, particularly at 91 days. Perturbation plots, contour maps, and gradient analyses indicated that SSS played a dominant role at later stages and that maintaining a proper balance among supplementary cementitious materials (SCMs) and aggregate replacements is crucial. The developed models and response surfaces provide practical guidance for designing slag-based mortars with improved mechanical properties and enhanced sustainability.

Microplastics (MPs) pollution has become a global environmental issue. Soil, as a key environmental medium, serves as an important sink and carrier of MPs. Accurate and efficient extraction of MPs from soil matrices is essential for understanding their distribution, composition, and environmental behavior. This study presents a refined extraction method that combines two-step density separation with sodium chloride (NaCl, 1.20 g/cm³), hydrogen peroxide (H₂O₂) digestion for organic matter removal and a Fractionated Filtration Method (FFM) to capture MPs across multiple particle size ranges. Polymer identification and size characterization were performed using the high-throughput Agilent 8700 Laser Direct Infrared (LDIR) imaging system. Method validation demonstrated a recovery rate of 85% based on 100 μm MPs standards spiked into soil and minimal background contamination of 5–8 particles in blank controls, confirming the reliability of the workflow. Applying this method to agricultural soils from the Hetao Irrigation District revealed widespread MP contamination, with concentrations ranging from 5778 to 31,489 particles/kg and an average of 16,461 ± 8097 particles/kg. More than 99% of MPs were smaller than 500 μm, with the 10–30 μm fraction dominating the distribution. Polypropylene (PP), polyamide (PA), and polyethylene (PE) accounted for over 90% of detected MPs. This refined method enables reproducible extraction and accurate characterization of fine MPs in complex soil environments and provides a practical foundation for advancing standardized soil MP monitoring protocols.