International Concrete Abstracts Portal

Ultra-high-performance geopolymer concrete (UHP-GPC) can exhibit high to exceptional strength. Given the importance of UHP-GPC’s mechanical properties, the prediction of its 28d compressive strength (f’_c) remains insufficiently explored. This study predicts UHP-GPC’s f’_c based on alkali-activated materials, sand, fiber volume, water-to-geopolymer binder, and alkali activator ratios. Advanced statistical modeling and a spectrum of ensemble machine learning (ML) algorithms, including random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), and stacking, are utilized to predict UHP-GPC’s strength. The derived models reveal the significance of fiber, slag, and sand as the most significant factors influencing the 28d f’_c of UHP-GPC. All the ML models demonstrate higher precision in forecasting f’_c of UHP-GPC compared to statistical modeling, with R²s peaking at 0.85. Equations are derived to predict the strength of UHP-GPC. This article reveals that UHP-GPC with superior mechanical properties can be designed for further sustainability.

Geopolymer concrete (GPC) is a progressive material with the capability to significantly reduce global industrial waste. The combination of industrial by-products with alkaline solutions initiates an exothermic reaction, termed geopolymerization, resulting in a carbon-negative concrete that lessens environmental impact. Fly ash (FA)-based GPC displays noticeable variability in its mechanical properties due to differences in mixture design ratios and curing methods. To address this challenge, the authors optimized the constituent proportions of GPC through a meticulous selection of nine independent variables. A thorough experimental database of 1242 experimental observations was assembled from the available literature, and artificial neural networks (ANNs) were employed for compressive strength modeling. The developed ANN model underwent rigorous evaluation using statistical metrics such as R-values, R2 values, and mean squared error (MSE). The statistical analysis revealed an absence of a direct correlation between compressive strength and independent variables, as well as a lack of correlation among the independent variables. However, the predicted compressive strength by the developed ANN model aligns well with experimental observations from the compiled database, with R2 values for the training, validation, and testing data sets determined to be 0.84, 0.74, and 0.77, respectively. Sensitivity analysis identified curing temperature and silica-to-alumina ratio as the most crucial independent variables. Furthermore, the research introduced a novel method for deriving a mathematical expression from the trained model. The developed mathematical expressions accurately predict compressive strength, demonstrating minimal errors when using the tan-sigmoid activation function. Prediction errors were within the range of –0.79 to 0.77 MPa, demonstrating high accuracy. These equations offer a practical alternative in engineering design, bypassing the intricacies of the internal processes within the ANN.

The geometry of arched (vertically curved) reinforced concrete (RC) members contributes to the development of additional stresses, affecting their flexural and shear strengths. This aspect of curvilinear RC members reinforced with glass fiber-reinforced polymer (GFRP) bars has not been reported in the literature. In addition, no specific design recommendations consider the effect of curvilinearity on the flexural and shear strengths of curved GFRP-RC members. This study has performed pioneering work in developing models to predict the flexural and shear strengths of curvilinear GFRP-RC members, with a focus on precast concrete tunnel lining segments. Eleven full-scale curvilinear GFRPreinforced tunnel segment specimens were tested under bending load as the experimental database. Then, a model was developed for predicting the flexural strength of curvilinear GFRP-RC members. This was followed by the development of two shear-strength prediction models based on the Modified Compression Field Theory (MCFT) and critical shear crack theory (CSCT). After comparing the experimental and analytical results, a parametric study was performed to evaluate the effect of different parameters on the flexural and shear strengths of curvilinear GFRP-reinforced members. The results indicate that neglecting the curvilinearity effect led to a 17% overestimation of the flexural strength, while the proposed models could predict the flexural strength of the specimens accurately. The proposed models based on the MCFT—referred to as the semi-simplified Modified Compression Field Theory (SSMCFT) and the improved simplified Modified Compression Field Theory (ISMCFT)—predicted the shear strength of the specimens with 28% conservativeness. In addition, the modified critical shear crack theory (MCSCT) model was 10% conservative in predicting the shear strength of curvilinear GFRP-RC members.

Extensive experimental verification has shown that the use of fiber-reinforced polymer (FRP) anchors in combination with externally bonded FRP composites increases the flexural capacity of existing reinforced concrete (RC) structures. Thus, a rational prediction model is introduced in this study so that fiber splay anchors may be accurately designed for practical strengthening applications. Simplified structural mechanics principles are used to build this model for capacity prediction of a group of fiber splay anchors used for FRP flexural strengthening. Three existing test series using fiber splay anchors to secure FRP-strengthened T-beams, block-scale, and one-way slabs were used to calibrate and verify the accuracy and applicability of the present model. The present model is shown to yield very accurate predictions when compared to the results of the block-scale specimen and eight different one-way slabs. The proposed model is also compared with the predictions of a design equation adapted from the case of channel shear connectors in composite concrete-steel construction. Results show a very promising correlation.

This study addressed a critical knowledge gap by examining the influence of staggering on the bond strength of lapped glass fiber-reinforced polymer (GFRP) bars in concrete members. It involved a comprehensive investigation of new-generation GFRP bars with varying staggering configurations in nine large-scale GFRP-reinforced concrete (RC) beams with a rectangular cross section of 300 x 450 mm (11.8 x 17.7 in.) and a length of 5200 mm (204.7 in.). The tests investigated splice strength with three staggering distances: 0, 1.0, and 1.3 times the splice length (ls) from center-to-center of two adjacent splices, and three splice lengths of 28, 38, and 45 times the bar diameter (db). Results revealed a slight improvement in ultimate load-carrying capacity (less than 10%) for partially and fully staggered splices compared to non-staggered ones, with the latter exhibiting a more ductile failure mode. The effect of staggering was consistent across different splice lengths, demonstrating that splice length was not a factor. Although staggering reduced flexural crack width, it increased the total number of cracks due to expanded splice regions. Bond strength improved with staggering, with gains of 4.0% and 8.0% for partially and fully staggered splices, respectively. ACI CODE- 440.11-22 provides more accurate predictions of the bond strength of lap-spliced GFRP bars than the other design codes, showing an average test-to-prediction ratio of 1.03 for non-staggered splices. Nevertheless, it requires some reconsiderations when it comes to staggered splices. To address this, a proposed modification factor was introduced to account for staggering conditions when calculating bond strength and splice length in ACI CODE-440.11-22.