Monitoring of gradual increase in elastic modulus of concrete over time is crucial for designing structures exposed to early age loading and predicting long-term deformations, such as creep. Two primary methods are used to assess elastic modulus: the static method, involving compression tests, and the dynamic method, utilizing approaches like EMM-ARM (E-modulus Measurement through Ambient Response Method), impact-echo, and ultrasonic approach. The static method, although destructive, yields the static or secant modulus, directly applicable for structural design. However, it cannot be utilized to track changes in elastic modulus within the existing structure caused by factors such as hydration, freeze-thaw, or chemical attack. In contrast, the non-destructive dynamic method can monitor these changes in the existing structure. Yet, the elastic modulus obtained through this method, known as the dynamic elastic modulus, represents the initial tangent modulus and is generally higher than the static modulus. To estimate the static elastic modulus through the non-destructive ultrasonic approach, we propose a new signal processing technique using direct wave interferometry (DWI) in this study. To validate the elastic modulus estimated through this technique, embeddable ultrasonic sensors are installed in the specimen within the temperature stress testing machine (TSTM). The experimental results show that the elastic modulus derived from the newly proposed DWI-based ultrasonic approach consistently provides more accurate estimates of the static elastic modulus compared to the UPV-based dynamic elastic modulus. The relative errors between the DWI-based and compression test-based elastic moduli on 7-day is 2.6 %. This approach also enables the tracking of static elastic modulus changes due to freeze-thaw cycles or chemical attacks.

Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO₂ emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly correlate the AAC mix design parameters to their performances through an artificial intelligence approach. To be specific, 145 fresh property data and 193 mechanical strength data were collected from laboratory tests on 52 AAC mixtures, which were used as inputs for the machine learning algorithm. Five independent random forest (RF) models were established, which are able to predict fresh and hardened properties (in terms of compressive strength, slump values, static/dynamic yield stress, and plastic viscosity) of AAC with equivalent accuracy reported in the literature. Moreover, an inverse optimization was performed on the RF model obtained to reduce the sodium silicate dosages, which may further mitigate the environmental impact of producing AAC. The present RF model gives practical information on AAC mix design cases.

This study aims to experimentally investigate the autogenous deformation and the stress evolution in restrained high-volume ground granulated blast furnace slag (GGBFS) concrete. The Temperature Stress Testing Machine (TSTM) and Autogenous Deformation Testing Machine (ADTM) were used to study the macro-scale autogenous deformation and stress evolution of high-volume GGBFS concrete with w/b ratios of 0.35, 0.42, and 0.50. The early-age cracking (EAC) risk (quantified by stress-strength ratio) and stress relaxation were analyzed extensively based on ADTM and TSTM results. Furthermore, Environmental Scanning Electron Microscopy (ESEM), X-ray Diffraction (XRD), and Mercury Intrusion Porosimetry (MIP) were conducted to explore the micro-scale origin of the autogenous deformation of high-volume GGBFS concrete, which supports the observations on the macroscale measurement of TSTM/ ADTM tests. This study finds that the ettringite formation in the first two days results in autogenous expansion, which can delay the appearance of tensile stress. The magnitude of autogenous expansion depends on the compatibility of ettringite content and pore size. The w/b ratio of 0.42 turns out to be optimal because it produces the highest amount of ettringite and results in the highest autogenous expansion. In comparison, the w/b ratio of 0.35 introduces significant autogenous shrinkage after the expansion peak and therefore corresponds to a high early-age cracking risk.

Coda wave interferometry (CWI) holds promise as a technique for concrete stress monitoring. This is because the coda, which consists of multiply scattered arrivals, is the result of propagation through the medium over large distances. As such, it is sensitive to both minute structural changes and small velocity changes in that medium. Previous studies focusing on concrete have predominantly utilized the time-domain-based stretching technique to measure travel-time changes. There is, however, a lack of consensus on how to quantify these changes effectively. In this study, we conduct a systematic comparison between two techniques, namely the stretching technique and the wavelet cross-spectrum (WCS) technique, for measuring stress-induced velocity changes in a cylindrical concrete sample. Our comparison focuses on two key aspects: (i) stability against cycle skipping and (ii) consistency in retrieving velocity changes. Experimental results reveal that both the WCS technique and the stretching technique yield consistent velocity changes. In terms of stability, it is challenging to determine which technique performs better, due to differences in the mechanisms triggering cycle skipping. However, when considering waves with frequencies ranging from 50 kHz to 80 kHz, both techniques exhibit comparable performance. Based on our findings, we offer the following recommendations for utilizing these CWI techniques in concrete stress monitoring: For the stretching technique, selecting the time window length based on the wave frequency and the expected magnitude of velocity change. For the WCS technique, operating it in the frequency band where spectral decomposition shows sufficiently high energy in the signal and can accommodate the expected magnitude of velocity change.