Ultra-high performance concrete (UHPC) is a novel material which shows impressive properties including high strength, increased toughness and excellent durability. One of the potential applications of UHPC is in heavily-loaded beams and bridge girders where their use can allow for more efficient design sections and increased durability. On the other hand, the high bond capacity of UHPC can eventually lead to brittle bar fracture failures in flexural members, especially when combined with low or moderate amounts of ordinary steel reinforcement (ρ ≤ 1%). This paper examines the influence of reinforcement grade on the flexural behaviour of UHPC beams. As part of the study, a series of UHPC beams built with either Grade 400 MPa ordinary steel reinforcement, Grade 690 MPa high-strength reinforcement or Grade 520 MPa stainless steel reinforcement are tested under four-point bending. The main parameters investigated include the influence of UHPC, steel type and tension steel ratio. Overall the results show that the ductility of the UHPC beams is influenced by both the tension steel ratio and steel grade/type. The results also show the benefits of combining UHPC with higher grade or higher ductility steel reinforcement.

Major bridges are requiring extended service lives of 100 years or more. This requires the use of high performance concretes and often enhanced corrosion protection provided by improved corrosion resistance of the reinforcing bars by using alloying, coatings, and/or corrosion inhibitors. Producing the entire bridge deck out of high performance concrete can lead to excessive cracking due to autogenous and drying shrinkage. Though this can be reduced by using shrinkage reducing admixtures or lightweight fines, the cost to implement these techniques for a full deck is high. However, a high performance concrete overlay uses considerably less high performance concrete, and as such can reduce the overall cost of the bridge deck and potentially allow for use of a more user friendly, less costly base concrete. This paper models the service life of a bridge deck using a high performance overlay. A probabilistic approach is used and the effect of cracking is included.

A computational framework employing machine learning (ML) is applied to predict failure mode of ultra-high-performance concrete (UHPC) beams. For this purpose, results from a number of tests on UHPC beams with different geometric and loading configurations and material characteristics are collected and utilized as an input to the ML framework. Results from numerical studies are not included in the data set due to the fact that they are highly dependent upon the adopted material models, meshing practices, as well as other assumptions used in modeling. Artificial neural network is used to predict the failure mode of the UHPC beams. Results indicate that the proposed ML framework is capable of predicting failure mod of UHPC beams with varying reinforcement and configurations, and can be considered for use in design applications. This paper aims to promote the applicability of ML for a practical engineering problem, detecting structural response of UHPC beams.

Concrete and cement-based materials inherently possess an autogenous self-healing capacity, which is even higher in High- and Ultra-High-Performance Concrete (HPC, UHPC) because of the high content of cement and supplementary cementitious materials (SCM) and low water/binder ratios. In this study, quantitative correlation through statistical models have been investigated based on the meta-data analysis. The employed approaches aim at establishing a correlation between the mix proportions, exposure type, and time and width of the initial crack against suitably defined self-healing indices. This study provides a holistic investigation of the autogenous self-healing capacity of cement-based materials based on extensive literature data mining. This is also intended to pave the way towards consistent incorporation of self-healing concepts into durability-based design approaches for reinforced concrete structures. The study has shown that the exposure type and duration, crack width size, and chemical admixtures have the most significant promotion on self-healing indices. However, other parameters, such as fibers and mineral admixtures have less impact on the autogenous self-healing of UHPC. The study also proposes suitably built design charts to quickly predict and evaluate the self-healing efficiency of cement-based materials which can significantly reduce, in the design stage, the time and efforts of laboratory investigation.

This paper presents and explains an implementation of artificial intelligence for the real-time crack detection of ultra-high-performance concrete (UHPC). A deep learning algorithm is employed to process image data and to identify physical cracks. The state-of-the-art object detection method generates accurate results with small datasets. To provide training and validation images, UHPC specimens are cast with various fibers and loaded per an ASTM standard, including steel and synthetic (collated and monofilament polypropylene) fibers. After testing, sample images are labeled with an annotation tool and the algorithm is trained and validated with an image recognition approach, leading to a mean average precision (mAP) of 99%. The occurrence of cracking and propagation are linked with the applied load level to appraise the influence of the mixed fibers in the crack development of UHPC. It needs to be noted that the adopted deep learning architecture is incapable of quantifying crack width and area directly; therefore, a Java-based image processing program is used to measure these properties of the specimens. The characteristics of the load-induced cracks are dominated by the fiber types. Plain UHPC fails rapidly and the flexural capacity of UHPC increases with the presence of the fibers; especially, the UHPC with steel fibers demonstrates higher flexural capacities than other cases.