International Concrete Abstracts Portal

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 in 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 commonly 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 normal-weight 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 that of 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.

In this study, the challenge of automating concrete crack image classification by developing a lightweight machine-learning model that balances accuracy with computational efficiency was addressed. Traditional deep learning models, while accurate, suffer from high computational demands, limiting their practicality in on-site applications. This study’s approach used the Random Forest (RF) classifier combined with a Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) for feature extraction, offering a more feasible alternative for real-time structural health monitoring. Comparative analysis with the Convolutional Neural Network (CNN) model highlights our model’s significantly reduced size and inference times, with only a marginal compromise in accuracy. The results demonstrated that the RF models, particularly RF with LBP, are well-suited for integration into resource-constrained environments, paving the way for their deployment in portable, on-site diagnostic systems in civil engineering. This study contributed a novel perspective to the field, emphasizing the importance of efficient machine learning solutions in practical applications of structural health monitoring.

Recent research data was evaluated with the aim of extending the applicability of using deformed steel fiber-reinforced concrete (SFRC) to enhance the shear strength of beams and one-way slabs. Experimental results were assessed for influences on the shear strength of SFRC members that do not contain stirrups of factors, including size effect, concrete density (normalweight and lightweight) and compressive strength, fiber-volume fraction (Vf), and the longitudinal steel reinforcement ratio. Estimates of steel stresses in longitudinal bars at the time of shear failure were carried out to identify differences in members with distinct longitudinal steel ratios and bar grades, consistent with the range of flexural design parameters in ACI 318-19. Results of these analyses and a reliability investigation of design equations applicable to members without fibers were used for proposing new provisions for the shear design of SFRC beams and one-way slabs based on the ACI 318-19 shear-strength model.

Compared to external curing, internal curing enables the judicious use of available water to provide additional moisture in concrete for more effective hydration and improvement in the performance of concrete structures. However, certain challenges with the incorporation of internal curing materials (ICMs) still need to be addressed, as their effectiveness depends on several factors. Furthermore, sustainable construction demands the use of recycled materials, and this paper discusses the comparative evaluation of recycled aggregate (RA) as an ICM, along with two other types of ICMs, on various properties of high-performance concrete in the hardened state under two curing conditions. Concrete mixtures were prepared with pre-wetted RAs, superabsorbent polymers (SAPs), and pre-wetted lightweight volcanic aggregates (LWVAs) as ICMs. Concrete performance was compared through the investigation of the strength development, shrinkage, mass loss, and volumetric water absorption. In addition, the change in internal humidity of concrete with time at different stages of hardening was determined. The compressive strength results showed that RA and LWVA are more efficient in early days, and the performance of SAP is better in the later age due to its slow water releasing capabilities. Compared to the control mixture, the least reduction in strength of 4% and 8% at 28 days and 90 days, respectively, could be observed for the mixtures containing RA under both air and water curing.

Lightweight concrete (LWC) finds wide-ranging applications inthe construction industry due to its reduced dead load, good fireresistance, and low thermal and acoustic conductivity. Lightweightgeopolymer concrete (LWGC) is an emerging type ofconcrete that is garnering attention in the construction industryfor its sustainable and eco-friendly properties. LWGC is producedusing geopolymer binders instead of cement, thereby reducing thecarbon footprint associated with conventional concrete production.However, the absence of standard codes for geopolymer concreterestricts its widespread application. To address this limitation,an investigation focused on developing a new mixture design forLWGC by modifying the existing ACI 211.2-98 provisions has beencarried out. In this study, crucial parameters of LWGC, such asalkaline-binder ratio (A/B), molarity, silicate/hydroxide ratio, andcuring temperature, were established using machine learning techniques. As a result, a simple and efficient method for determining the mixture proportions for LWGC has been proposed.