Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

This study aims to investigate the effect of restraint configuration on crack formation due to shrinkage-and-creep-induced volumetric change in unbonded post-tensioned slabs. The first part of this study focuses on the comparison of existing shrinkage and creep calculation models that are used to predict the volume-changing behavior of concrete. The second part of this study presents the finite element analysis of a series of architectural configuration prototypes subjected to shrinkage and creep, which comprise unbonded post-tensioned slabs with various restraint configurations. The shrinkage and creep effects were simulated in the analysis by imposing strains obtained from one selected calculation model. The results suggest that a slab up to 300 ft. (90 m) in length does not require a closure strip if it is unrestrained by perimeter walls, and that the most effective restraint crack mitigation strategy for a slab restrained by perimeter walls is a partial wall release.

Reinforced concrete (RC) bridge columns are typically designated as the primary source of energy dissipation for a bridge structure during an earthquake. Therefore, seismic repair of RC bridge columns has been studied extensively during the past several decades. On the other hand, few studies have been conducted to evaluate how repaired column members influence the system-level response of an RC bridge structure in subsequent earthquakes. In this study, a numerical model was established to simulate the response of two large-scale RC columns, repaired using different techniques, reported in the literature. The columns were implemented into a prototype bridge model that was subjected to earthquake loading. Incremental dynamic analysis (IDA) and fragility analysis were conducted on numerical bridge models to evaluate the efficacy of the repairs and the post-repair seismic performance of the prototype bridge that included one or more repaired columns in various locations. For the prototype bridge herein modeled, the results showed that a confinement-enhanced oriented repair would not affect the seismic behavior of the prototype bridge. Increasing the strength of the longitudinal reinforcement could effectively reduce the drift of the prototype bridge in subsequent earthquakes. A full repair configuration for the columns was the most effective method for enhancing the seismic performance of the prototype bridge. To obtain a positive effect on seismic performance, a minimum of two repaired columns was required.

In this study, a novel mountain train system was developed that can run along a steep gradient of 180 ‰ and sharp curve with a minimum radius of 10 m. For this novel mountain train, an embedded precast concrete rack rail track was implemented to share the track with an automobile road and increase constructability in mountainous regions. The embedded rack rail track is connected to a hydraulically stabilized base (HSB) layer with shear anchors, which must have sufficient longitudinal resistance because they bear most of the traction forces originated from the rack rail and longitudinal loads owing to the steep gradient. In addition, the damage to the shear anchor parts, including the surrounding concrete, must be strictly limited under the service load because the maintenance of shear anchors inside the track is extremely difficult after installation. In this study, the focus was made on the shear anchor behavior and design an embedded rack rail track, considering the serviceability and ultimate limit states. Accordingly, the design loads for mountain trains were established, and the serviceability criteria of the anchor were proposed. Subsequently, the resistance and damage of the shear anchors were evaluated and analyzed based on the results of several finite element analyses. Finally, the design method of the shear anchors for the embedded rack rail track was established and verified.

As concrete dominates the construction industry, alternatives to traditionally used steel reinforcement are being sought. This study explored the suitability of carbon fiber-reinforced polymer (CFRP) as a substitute within rigid frames, focusing on its impact on section ductility and overall structural durability against seismic events. However, current design guidelines address quasi-static loads, leaving a gap for dynamic or extreme circumstances. Our approach included multiscale simulations, parametric study, and energy dissipation analyses, drawing upon a unique adaptation of modified compression field theory. In our efforts to optimize macro and microparameters to improve yield strength, manage brittleness, and govern failure modes, we also recognized the potential of CFRP's high corrosion resistance. This characteristic of CFRP could significantly reduce the frequency of required repairs, thereby contributing to enhanced durability of the structures. The research reveals that CFRP's durability and seismic resistance are attributed to plastic joints within compressed fibers. Notably, CFRP can impart ductility to structural designs, effectively balancing its inherent brittleness, particularly when integrated with quasi-brittle materials. This research challenges the notion that designing bendable components with carbon fiber reinforcement is impractical. It shows that creating ductile bending components with CFRP in concrete is feasible despite the material's brittleness. This funding overturns conventional assumptions and opens new avenues for using CFRP in structural applications where ductility and resilience are crucial.

This study nondestructively examined the evolution of crack density in ultra-high performance concrete (UHPC) upon cyclic loading. Uniaxial compression was repeatedly applied to the cylindrical specimens at levels corresponding to 32% and 53% of the maximum load-bearing capacity, each at a steady strain rate. At each stage, both P-wave and S-wave velocities were measured in the absence of the applied load. In particular, the continuous monitoring of P-wave velocity from the first loading prior to the second loading allowed real-time observation of the strengthening effect during loading and the recovery effect afterwards. Increasing the number of cycles resulted in the reduction of both elastic wave velocities and Young's modulus, along with a slight rise in Poisson's ratio in both tested cases. The computed crack density showed a monotonically increasing trend with repeated loading, more significant at 53% than at 32% loading. Furthermore, the spatial distribution of the crack density along the height was achieved, validating the directional dependency of microcracking development. This study demonstrated the capability of the crack density to capture the evolution of microcracks in UHPC under cyclic loading condition, as an early-stage damage indicator.

Using an extensive database, a sensitivity analysis across fifteen machine learning (ML) classifiers was conducted to evaluate the impact of various data manipulation techniques, evaluation metrics, and explainability tools. The results of this sensitivity analysis reveal that the examined models can achieve an accuracy ranging from 72-93% in predicting the fire-induced spalling of concrete and denote the light gradient boosting machine, extreme gradient boosting, and random forest algorithms as the best-performing models. Among such models, the six key factors influencing spalling were maximum exposure temperature, heating rate, compressive strength of concrete, moisture content, silica fume content, and the quantity of polypropylene fiber. Our analysis also documents some conflicting results observed with the deep learning model. As such, this study highlights the necessity of selecting suitable models and carefully evaluating the presence of possible outcome biases.

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

The Federal Highway Administration (FHWA) has recommended the use of AASHTOWare Pavement Mechanistic-Empirical Design (PMED) software for Roller-Compacted Concrete (RCC) pavement design, but specific calibration for RCC is missing. This study investigates the software's capacity to predict the long-term performance of RCC roadways within the framework of conventional concrete pavement calibration. By reanalyzing existing RCC projects in several U.S. states: Colorado, Arkansas, South Carolina, Texas, and Illinois, the study highlights the need for specific calibration tailored to the unique characteristics of RCC. Field observations have emphasized occurrence of early distresses in RCC pavements, particularly transverse-cracking and joint-related issues. Despite data challenges, the AASHTOWare PMED software exhibits notable correlation between its long-term predictions and actual field performance in RCC roadways. This study stresses that RCC applications with insufficient joint spacing and thickness are prone to premature cracking. To enhance the accuracy of RCC pavement design, it is essential to discuss the inclusion of RCC as a dedicated rigid pavement option in AASHTOWare PMED. This becomes particularly crucial when the rising popularity of RCC roadways in the U.S. and Canada is considered. Such an inclusion would solidify RCC as a viable third option alongside Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP) for design and deployment of rigid pavements. The research presents a roadmap for future calibration endeavors and advocates for the integration of RCC pavement as a distinct pavement type within the software. This approach holds promise for achieving more precise RCC pavement design and performance predictions.

In urban areas, appropriate backfilling design is necessary to prevent surface subsidence and subsurface cavities after excavation. Expandable foam grout (EFG), a mixture of cement, water, and an admixture, can be used for cavity filling because of its high flowability and volume expansion. EFG volume expansion induces a porous structure that can be quantified by the entrapped air content. This study observed the unit weight variations in the EFG before and after expansion depending on the various admixture-cement and water-cement ratios. Subsequently, the air content before and after expansion and the gravimetric expansion ratios were estimated from the measured unit weights. The air content before expansion linearly increased with an increase in the admixture-cement ratio, resulting in a decrease in the unit weight. The air content after the expansion and the expansion ratio increased nonlinearly, and the curves stabilized at a relatively high admixture-cement ratio. In particular, a reduced water-cement ratio limits the air content generation and expansion ratio, primarily because of the short setting time, even at a high admixture-cement ratio. Based on the results, the relationship between the maximum expansion ratio of EFG and the mixture ingredients (water-cement and admixture-cement ratios) was introduced.