International Concrete Abstracts Portal

The accurate identification of cracks is crucial in the research and practical use of strain-hardening cementitious composites (SHCC). The rise of deep learning techniques in computer vision has introduced efficient ways to automate crack detection processes. However, creating dataset for training these deep learning models demands a lot of effort and time, a situation worsened by intricate crack patterns. This study introduces a novel method using a hybrid generative adversarial network (HGAN) to simplify the task of detecting complex cracks. HGAN combines the strengths of deep convolutional generative adversarial network (DCGAN) and conditional generative adversarial network (CGAN), offering a solution for evaluating SHCC characterized by dense microcracks and conventional concrete with simpler cracks. Our findings demonstrate the method's effectiveness for SHCC with dense and microcracks, leading to enhanced precision in crack characterization, with an F1 score and intersection-over-union (IOU) for SHCC crack segmentation at 0.982 and 0.980, respectively.

Advancements in AI and computational models have significantly enhanced the predictability of concrete performance by leveraging extensive datasets. Recently, machine learning models have been developed to predict concrete’s compressive strength based on its mixture proportions. However, these models treat supplementary cementitious materials (SCMs) as a categorical (as opposed to quantitative) parameter and do not account for the significant impact of the SCM reactivity on concrete’s strength development. In this study, we assembled a dataset of binary (cement-SCM) mixtures, incorporating SCM reactivity measured by the R3 (ASTM C1897) test. Utilizing a random forest machine learning model, we demonstrated that integrating SCM reactivity significantly enhances the model's predictive performance with the fewest input parameters (w/cm, SCM/cm, SCM R3 heat, Agg/cm, cement CaO%). Further, we implemented a multi-objective Bayesian optimization framework to assist in the mixture proportioning of low-carbon low-cost concrete utilizing cement(s) and SCM(s) available to a concrete producer. This framework proposes concrete mix designs to meet a target 28-day compressive strength while minimizing cost and CO2 emissions, by leveraging SCMs with varied reactivity levels. The proposed mix designs were further validated with experiments. The work demonstrates how to avoid model extrapolation and erroneous predictions by utilizing a multi-dimensional convex envelop algorithm. Overall, the outcomes of this work provide a valuable tool for the concrete industry which can be expanded to predict and incorporate other metrics of concrete performance (e.g., workability, durability) and develop optimized mix designs accordingly.

This study investigates the ultimate flexural strength (UFS) of reinforced concrete beams strengthened with carbon fiber-reinforced polymer (CFRP) (RCB-SCFRP), focusing on the identification and quantification of flexural overstrength concerning the nominal flexural strength (NFS) as defined by ACI 440.2R. A total of 106 full-scale specimens tested were carefully selected from previous research, varying in concrete strength, reinforcement configurations, and CFRP materials from multiple manufacturers. Results show that ACI 440.2R provisions accurately and conservatively estimate the flexural capacity of CFRP-strengthened beams. Including CFRP transverse reinforcement (TR) resulted in a slight increase in UFS. The type of strengthening, whether preloaded and repaired or strengthened, had little effect on the UFS/NFS ratio. Steel reinforcement ratio (SRR) significantly influenced overstrength, with higher UFS/NFS ratios observed between 0.70% and 1.00% SRR. CFRP axial rigidity (Kf ρf) notably affected overstrength, with optimal performance between 0.10 and 0.50 GPa·mm. Deflection ductility was mainly affected by the rigidity of CFRP, with a 13% increase noted due to CFRP TR. A log-normal model was developed to estimate UFS for RCB-SCFRP beams based on experimental data and ACI 440.2R guidelines.

This study investigated the flexural behavior and seismic connection performance of precast lightweight aggregate concrete shear walls (PLCWs) using the relative emulation evaluation procedure specified by the Architectural Institute of Japan (AIJ). Six PLCW specimens connected through a bolting technique were prepared and tested under constant axial and cyclic lateral loads. In addition, three companion shear walls connected through the most-used spliced sleeve technique for precast concrete members were prepared to confirm the effectiveness of the bolting technique for the seismic connection performance. The main parameters were the concrete type (all-lightweight aggregate [ALWAC], sand- lightweight aggregate [SLWAC], and normalweight concrete [NWC]); the compressive strength of the concrete; and the connection technique. The test results showed that none of the specimens connected through the conventional spliced sleeve technique reached the allowable design drift ratio specified by the AIJ, indicating that the spliced sleeve is an unfavorable technique for obtaining a seismic connection performance of PLCWs equivalent to that of cast-in-place reinforced concrete shear walls. However, the specimens made of ALWAC or NWC and connected through the bolting technique not only reached the allowable design drift ratio specified by the AIJ but also satisfied the requirements of the seismic connection performance (lateral loads and allowable error at yield displacement) within the allowable design drift ratio. Consequently, the displacement ductility ratio of the specimens connected through the bolting technique was 1.52 times higher than those for the specimens connected through the conventional spliced sleeve technique, respectively. This difference was more prominent in the specimens made of ALWAC than in those made of SLWAC or NWC. Thus, the use of the bolting technique as a wall-to-base connection in shear walls can effectively achieve a seismic connection performance equivalent to that of cast-in-place shear walls while maintaining the medium-ductility grades.

Current design assumptions for precast, prestressed concrete piles embedded in cast-in-place (CIP) pile caps or footings vary across states, leading to inconsistencies in engineering practices. Previous studies suggest that short embedment lengths (0.5 to 1.0 times the pile diameter) can develop approximately 60% of the bending capacity of the pile, with full fixity potentially achieved at shorter embedment lengths than current design specifications due to confinement stresses. This study experimentally evaluates 10 full-scale pile-to-cap connection specimens with varying embedment lengths, aiming to investigate the required development length for full bending capacity. The findings demonstrate that full bending capacity can be achieved at the pile-to-pile cap connection with shallower embedment than code provisions, challenging existing design standards and highlighting the need for more accurate guidelines for bridge foundation design.