The most important parameter used to determine the frost resistance of concrete is the distance between air voids. The linear traverse method has been used to obtain this spacing factor as a representative distance parameter. One of the present authors has proposed a new method of evaluating the distance using point process statistics. In this study, the spacing factors independently obtained for 52 extant mixtures of concrete are compared with the characteristic distance defined by the nearest neighbor distance distribution function. For that, a procedure for obtaining the necessary parameters for the point process method from records of linear traverse measurements is proposed. There exists a strong correlation between the spacing factor and the characteristic distance. The difference between two distance parameters is at most a few tens of micrometers. Furthermore, the median distance simply defined by the nearest neighbor distance distribution function is also found to have a good correlation with the spacing factor. The characteristic distance or the median distance is representative of an air void system and is an alternate to the conventional spacing factor.

This paper is the English translation from the authors’ previous work [Igarashi, S., Taniguchi, M. and Yamashita, S., (2021). “Derivation of characteristic distances of the point process method from records of linear traverse measurement and their correlation with spacing factors.” Cement Science and Concrete Technology, 74, 131-138. (in Japanese)].

This study presents an analytical method for estimating the remaining shear capacity of reinforced concrete (RC) beams subjected to high temperature heating and verifies it by experiments. We focused on the mechanism by which the remaining shear capacity of members self-recovers in a humid environment after slow cooling. The authors confirmed that the infiltration of water vapor in the air into concrete after heating caused rehydration of quicklime (CaO) produced by high-temperature heating and self-recovery of material strength. Based upon material experiments, the mechanical model of calcium hydroxide formation by rehydration of quicklime was incorporated into the multi-scale analysis. Using the proposed model of this study, the effect of damage on the remaining strength of damaged members was also investigated. It was confirmed that the series of high temperature heating, slow cooling, moisture absorption, self-healing, and remaining shear capacity could be reproduced by the multi-scale analysis.

The main purpose of this paper is to predict analytically the concrete basic creep time-dependent deformation according to its composition and microstructure. Thus, concrete was modelled as a three-phase composite formed by aggregates surrounded by interfacial transition zones (ITZs) and embedded randomly in a hardened cement paste. First, the ITZ phase volume fraction within concrete was evaluated using an analytical formula that the authors had recently proposed (Zouaoui et al. 2017). Then, the specific basic creep deformation of concrete was evaluated analytically based on a four-sphere homogenization model where cement paste and ITZ have a linear viscoelastic behaviour whereas aggregates are assumed linear elastic.

The predictions of the proposed model show that cement paste basic creep and aggregates volume fraction and maximum packing density are the main parameters governing concrete basic creep. Moreover, these predictions show that aggregates gradation and ITZ thickness have secondary effects on concrete basic creep. Finally, the relevance and the validity of the proposed model have been discussed based on a comparison between its predictions and experimental results taken from literature and related to both normal and high-strength concretes.

Reinforced beams with deteriorated concrete can be repaired by replacing the deteriorated part or the entire beam using new concrete. Either of the two scenarios is decided to be used based on the degree and distribution of deterioration along the beam. This paper compares the structural performance of composite and monolithic reinforced concrete beams which represents the two cases of partial and entire replacement of concrete, respectively. In total, 10 beams with dimensions of 3200×250×400 mm (L×W×D) were constructed. The reference monolithic beams were cast with either self-consolidating concrete (SCC) or fiber-reinforced self-consolidating concrete (FR-SCC). Composite beams were cast with conventional vibrated concrete (CVC) to a depth of 275 mm from the top then with either SCC or FR-SCC for the remaining third of the beam’s depth (125 mm) at the bottom. The composite beams were prepared to sim-ulate beams repaired in the tension zone after the removal of the deteriorated concrete. The test variables were fiber inclusion, fiber type, and beam type. One hybrid, one steel, and two polypropylene fiber types were employed in the FR-SCC. All fiber types were added at 0.5% by volume. The beams were simply supported and were loaded in four-point bending. Test findings indicate that both composite and monolithic beams exhibited similar cracking patterns at failure. However, the crack width of composite beams was lower due to enhanced fiber orientation along the tension zone and concrete confinement during the casting process. The structural performance of the beams was found to be mainly governed by mechanical characteristics of the fibers in the case of monolithic beams and mainly by the fiber length in the case of composite beams where fibers take preferential orientation during casting in the repair zone.

Artificial intelligence technology has super high-dimensional nonlinear computing capabilities, intelligent comprehensive analysis and judgment functions, and self-learning knowledge reserve expression functions. It can unlock the potential of high-dimensional nonlinearity relation between tangible components and performance indicators when compared to the empirical formula generated from classic statistical approaches. This article summarizes the types of artificial intelligence algorithms used to predict concrete performance, comprehensively sorts out the research progress of artificial intelligence technology in predicting the mechanical properties, work performance, and durability of concrete, and compares and analyzes the effects of algorithm selection, sample data, and model construction on the concrete compressive prediction system. The analysis shows that artificial intelligence technology has obvious advantages in measurement accuracy in predicting concrete performance compared to conventional statistical methods. Multiple algorithms should be used to cross-validate the model prediction findings. For tiny data sets, support vector machines are utilized. Decision tree evolution techniques should be used in algorithm models that require feature optimization or dispersed index prediction. Artificial neural networks can be used to solve different challenges. To improve the prediction model and boost its prediction accuracy, measures such as optimized features, integrated algorithms, hyperparameter optimization, enlarged sample data set, richer data sources, and data pretreatment are proposed.