International Concrete Abstracts Portal

The production of carbonated aggregates from Class F fly ash (FA) is challenging due to its low calcium content, typically less than 10%. This study investigates the production of carbonated alkali-activated aggregates using FA and calcium carbide sludge (CCS). Sodium hydroxide was used as an activator and examined the effects of autoclave treatment on the properties of these aggregates. The optimal mixture, comprising 70% FA and 30% CCS, achieved a single aggregate strength of >5 MPa in autoclave carbonated (AC) aggregates, comparable to the strength obtained after 14 days of water curing in without autoclave carbonated (WAC) aggregates. Both AC and WAC aggregates exhibited a bulk density of 790 to 805 kg/m³ and CO₂ uptake of 12.5% and 13.3% in AC and WAC aggregates, respectively. FE-SEM and FT-IR analysis indicated the formation C-A-S-H gel in noncarbonated aggregates, while calcite and vaterite, along with N-A-S-H gel, formed in carbonated aggregate. Concrete incorporating AC and WAC aggregates exhibit compressive strengths of 39 and 38 MPa, with concrete density of 2065 kg/m³ and 2085 kg/m³, respectively. Furthermore, AC and WAC aggregate concrete showed a reduction in CO₂ emission of 18% and 31%, respectively, compared to autoclave noncarbonate (ANC) aggregate concrete. These findings highlight the potential of producing carbonated alkali-activated aggregates from FA and CCS as sustainable materials for construction applications.

This study characterized pitting corrosion in prestressed piles, linked it to stress concentration factors through ultimate strength tests, and incorporated the findings into a simple predictive damage assessment model. Six one-third-scale Class V concrete prestressed piles were exposed for 38 months to outdoor tidal cycles simulating a marine environment. At the end of exposure, 24 strands were extracted from the piles, and corrosion loss along the strands was quantified using a new Pascal’s law-based strand profiler. This identified regions of locally higher steel loss caused by pitting corrosion. The same data set was used to confirm gravimetric loss measurements by summing localized section losses over the specimen length. Profiler data was complemented by microscopic imaging to further define pitting geometry. Ultimate load tests were conducted to examine the effect of pitting on residual tensile strength and ductility. Similitude principles were used to develop a model for predicting in-service stress in pile strands using available inspection report crack width data.

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.

Design recommendations are presented for calculating the immediate deflection of cracked prestressed concrete members under service load. Inconsistency and sometimes confusion regarding the calculation of immediate deflection for the different approaches presently available highlight the need for a rational approach to computing deflection. The ACI 318-19 approach for reinforced (nonprestressed) concrete is broadened to include prestressed concrete. This involves the implementation of an effective moment of inertia taken together with an effective eccentricity of the prestressing steel used to define the effective curvature and/or camber from the prestressing force. Proposed revisions to ACI 318 are presented for prestressed Class T and Class C flexural members and clear steps are provided for calculating immediate deflection. The effectiveness of the new approach is validated against an extensive database of test results, showing reasonable accuracy and reliability in predicting deflections. The paper concludes with practical recommendations for implementation and a worked-out example to illustrate the proposed methodology. These findings aim to enhance the accuracy and consistency of deflection predictions in prestressed concrete design, contributing to better serviceability and performance of concrete structures.

This paper presents the implications of creep-fatigue interactions for the long-term behavior of bulb-tee bridge girders prestressed with either steel strands or carbon fiber-reinforced polymer (CFRP) tendons. A large amount of weigh-in-motion data incorporating 194 million vehicles are classified to realistically represent live loads. Computational simulations are conducted as per the engagement of discrete autonomous entities in line with time- dependent material models. In general, the properties of CFRP tendons vary insignificantly over 100 years; however, the stress range of CFRP responds to fatigue cycles. Regarding prestress losses, the conventional method with initial material properties renders conservative predictions relative to refined approaches considering time-varying properties. The creep and fatigue effects alter the post-yield and post-cracking responses of steel- and CFRP-prestressed girders, respectively. From deformational capability standpoints, steel-prestressed girders are more vulnerable to fatigue in comparison with CFRP-prestressed ones. It is recommended that the fatigue truck and the compression limit of published specifications be updated to accommodate the ramifications of contemporary traffic loadings. Although the operational reliability of both girder types is satisfactory, CFRP-prestressed girders outperform their steel counterparts in terms of fatigue safety. Technical findings are integrated to propose design recommendations.