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I DAMAGET i is utilized to correlate with vulnerability value ( I V i ) in this study using Equation (10): 10 HT ht Graph B 3.4. Correction: Optimizing high-performance fber-reinforced cementitious composites for improving bridge resilience and sustainability Robustness of the bridge is calculated as: 14 HT ht Graph The bridge collapsed on March 15, 2018, and the road was closed until March 24, 2018, when the debris was cleaned. [Extracted from the article]

• Effect of three TiO 2 incorporation methods on photocatalytic (PC) efficiency of ECC is studied. • Effect of three TiO 2 incorporation methods on mechanical properties of ECC is studied. • Porous microstructure promotes the PC efficiency by increasing the exposure surface of ECC. • Alkaline environment of ECC consumes nitrate acid and promotes PC reactions. Titanium dioxide (TiO 2) nanoparticles have been incorporated in concrete to impart photocatalytic (PC) properties, such as the self-cleaning and air purification functionalities, which are dependent on PC reactions near the exterior surfaces of the concrete exposed to light. This study experimentally investigates the effect of three TiO 2 incorporation methods and surface patterns on the air-purifying functionality and mechanical properties of PC Engineered Cementitious Composites (PC-ECC). The air-purifying functionality was evaluated by measuring the nitrogen oxides (NO x) concentration change with the presence of PC-ECC plate specimens exposed to ultraviolet (UV) irradiation; uniaxial tensile and four-point bending tests were conducted to evaluate the tensile and flexural properties of the PC-ECC plates; scanning electron microscopy (SEM) was used to characterize the surface morphology of the plates; energy dispersive spectroscopy (EDS) was used to map the element distribution on the surface. Test results indicate that the amount of TiO 2 nanoparticles is reduced by 90% when TiO 2 is incorporated through a thin layer on the exterior surface of the ECC plate, while PC functionality and mechanical properties are retained. Incorporating TiO 2 through polyurethane coating reduces the NO x abatement efficiency by 70–80%, compared with directly mixing the TiO 2 nanoparticles in ECC. The reduced PC efficiency is attributed to the dense microstructure and low alkali content of the coating. This study advances the fundamental knowledge for designing functionally graded PC concrete with optimized PC efficiency and mechanical properties. [ABSTRACT FROM AUTHOR]

• Shear behaviors of studs embedded in ECC are studied. • Effects of stud height-to-diameter ratio on the shear behaviors of the studs are evaluated. • Formulas are provided to predict the shear capacity of studs embedded in ECC. Engineered Cementitious Composite (ECC) exhibits superior mechanical properties such as high tensile strength, ductility, and toughness, which enable using thinner sections in steel-ECC composite structures. Existing design equations for the shear capacity of stud connectors were derived by regression analysis of test data from push-out specimens using normal concrete, which often have stud height-to-diameter ratios above 4. This research investigates the shear behaviors of stud connectors, with different height-to-diameter ratios from 2.73 to 4.62, embedded in ECC. Push-out tests showed that the height-to-diameter ratio is important for the shear capacity of studs. Finite element analysis was performed to evaluate the effects of the diameter, height-to-diameter ratio, and tensile strength of studs as well as the compressive properties of ECC on the shear behaviors. Regression analysis was performed using the analysis data to modify the equations for predicting the shear capacity of studs embedded in ECC for steel-ECC composite structures. [ABSTRACT FROM AUTHOR]

• Auto-discovery of low-carbon ultra-high performance concrete (UHPC) is achieved. • Predictive models are established based on synthetic data and automated machine learning. • Compressive and flexural strengths, mini-slump spread, and porosity of UHPC are predicted. • Carbon footprint, embodied energy, cost, and mechanical properties are optimized. • New UHPC mixtures are discovered by evolutionary many-objective optimization. This paper presents an AI-guided approach to automatically discover low-carbon cost-effective ultra-high performance concrete (UHPC). The presented approach automates data augmentation, machine learning model generation, and mixture selection by integrating advanced techniques of generative modeling, automated machine learning, and many-objective optimization. New data are synthesized by generative modeling and semi-supervised learning to enlarge datasets for training machine learning models that are automatically generated to predict the compressive strength, flexural strength, mini-slump spread, and porosity of UHPC. The proposed approach was used to explore new UHPC mixtures in two design scenarios with different objectives. The first scenario maximizes the compressive and flexural strengths and minimizes porosity while retaining self-consolidation. The second scenario minimizes the life-cycle carbon footprint, embodied energy, and material cost, besides the objectives of the first scenario. The life-cycle carbon footprint, embodied energy, and material cost of the UHPC in the second scenario are respectively reduced by 73%, 71%, and 80%, compared with the UHPC in the first scenario. This research advances the capability of developing cementitious composites using AI-guided approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

As an increasing amounts of waste glass are produced annually, recycling glass in manufacturing concrete shows economic and environmental benefits. Recently, glass has been used as one of the ingredients to prepare high-performance fiber-reinforced cementitious composites (HPFRCCs), like strain-hardening cementitious composite and ultra-high-performance concrete, featuring high mechanical properties and long-term durability. While the application of glass particles may reduce the material cost, however, fundamental knowledge of the effects of using glass to replace concrete components on the key properties (e.g., mechanical properties, fresh properties, durability) and the underlying mechanisms is still lacking. This study aims to reveal the roles of waste glass particles on the key properties, clarify the fundamental mechanisms, and point out viable strategies to enhance the key properties of HPFRCCs incorporating glass. To this end, the paper reviews existing studies on using glass in preparing HPFRCCs and discusses possible methods to develop HPFRCCs with glass particles. While there are different types of glass, this review focuses on soda-lime glass, which is representative among all types of glass. At last, life cycle analysis is conducted to assess the effect of using glass particles on reducing the cost, CO 2 emission, and energy consumption. This review aims to enhance the fundamental knowledge of HPFRCC and propose further research of HPFRCCs incorporating glass. • Chemical reaction and size effect of glass particles on alkali-silicate reaction are elaborated. • Influence of glass on mechanical and fresh properties of HPFRCCs are elaborated. • New perspectives on durability in relation to using glass particles in HPFRCC are presented. • Fundamental mechanisms of HPFRCCs incorporating glass particles are elucidated. • Life cycle analysis is performed to estimate the sustainability of using glass particles in HPFRCCs. [ABSTRACT FROM AUTHOR]

Current development of high-performance fiber-reinforced cementitious composites (HPFRCC) mainly relies on intensive experiments. The main purpose of this study is to develop a machine learning method for effective and efficient discovery and development of HPFRCC. Specifically, this research develops machine learning models to predict the mechanical properties of HPFRCC through innovative incorporation of micromechanics, aiming to increase the prediction accuracy and generalization performance by enriching and improving the datasets through data cleaning, principal component analysis (PCA), and K-fold cross-validation. This study considers a total of 14 different mix design variables and predicts the ductility of HPFRCC for the first time, in addition to the compressive and tensile strengths. Different types of machine learning methods are investigated and compared, including artificial neural network (ANN), support vector regression (SVR), classification and regression tree (CART), and extreme gradient boosting tree (XGBoost). The results show that the developed machine learning models can reasonably predict the concerned mechanical properties and can be applied to perform parametric studies for the effects of different mix design variables on the mechanical properties. This study is expected to greatly promote efficient discovery and development of HPFRCC. [ABSTRACT FROM AUTHOR]

In cement composites, usually, reinforcement is provided to restrict the crack development and their further propagation under service conditions. Typically, reinforcements utilized in cementitious composites range from nanometer scale to millimeter scale by using nano-, micro-, and millimeter-sized fibers and particles. These reinforcements provide the crack arresting mechanisms at the nano/microscale and restrict the growth of the cracks under service loads, but usually, the synthesis of nano/microfibers, and afterward their dispersion in the cementitious materials, pose difficulty, thus limiting their vast application in the construction industry. Carbonaceous inerts are green materials since they are capable of capturing and storing carbon, thus limiting the emission of CO2 to the atmosphere. In the present study, a comprehensive review of the synthesis of low cost and environmentally friendly nano/micro carbonaceous inerts from pyrolysis of different agricultural/industrial wastes, and afterward, their application in the cementitious materials for producing high performance cementitious composites is presented, which have the potential to be used as nano/micro reinforcement in the cementitious matrix. [ABSTRACT FROM AUTHOR]

This paper presents a generative artificial intelligence (AI) approach to generate images of strain-hardening cementitious composite (SHCC) with complex crack patterns such as dense microcracks. This approach is developed to address the challenge of lacking data for training deep learning models used to automatically measure cracks in SHCC. The development of the approach is based on a framework which results in a hybrid generative adversarial network (HGAN) that seamlessly integrates a deep convolutional generative adversarial network (DCGAN) for generating images and a conditional generative adversarial network (CGAN) for labelling images. From the results, it was found that this approach provided high-quality labelled images automatically, and using these images significantly improved the accuracy of the deep learning models for measuring cracks in SHCC. The F1 score and Intersection Over Union (IOU) for crack segmentation reached 0.982 and 0.980, respectively. This approach will significantly promote crack measurement for SHCC materials and structures. [Display omitted] • A generative computer vision approach is developed for intelligent characterization of cracks. • Automatic generation and labelling of images with complex cracks are realized. • The artificial images with complex cracks are utilized to improve deep learning models. • The developed approach achieves high accuracy and high efficiency. • Dense microcracks in strain-hardening cementitious composites are successfully assessed. [ABSTRACT FROM AUTHOR]

Highlights • HPFRCC is used to fabricate beam/column joints in two-bay two-story frames. • Frames with HPFRCC joints are exposed to ISO-834 fire for one hour to simulate fire hazard. • Post-fire seismic behavior of reinforced concrete frames is experimentally investigated. • Effect of using HPFRCC joints on post-fire seismic behavior of frames is evaluated. • Effect of different fire scenarios on post-fire seismic behavior is evaluated. Abstract High-performance fiber-reinforced cementitious composite (HPFRCC) has potential to greatly improve the fire resistance and seismic behavior of concrete structures. This paper reports an experimental investigation on post-fire seismic behavior of two-bay two-story frames with HPFRCC joints. Four reinforced concrete frames were fabricated; three of them were tested in compartment fire for 60 min. The fire was regulated following ISO-834 temperature curve. Two different fire scenarios (one- and two-bay fire) were investigated. Two frames were made of monotonic conventional concrete; the other two frames had HPFRCC joints. Each frame was tested under a constant vertical load and a pseudo-static cyclic horizontal load with increased magnitude until the frame failed. The effects of the HPFRCC and fire scenarios on the failure mechanism, hysteretic loops, envelope curve, stiffness degradation, and energy dissipation of the frames were evaluated. The experimental results revealed that the fire exposure reduced the load capacity and deformability of the frames. In the two-bay fire scenario, the use of HPFRCC joints increased the post-fire load capacity by 11%, ultimate deformation by 6%, initial stiffness by 30%, and energy dissipation by 21%. The cyclic behavior of the frame in one-bay fire was better than that in two-bay fire. The frames with HPFRCC joints demonstrated better cyclic behaviors than the virgin reinforced concrete frame. [ABSTRACT FROM AUTHOR]

Highlights • Static and fatigue behaviors of studs embedded in ECC were studied by push-out tests. • Characterizing mechanical behavior of short studs embedded in ECC. • S-N relationships were developed for fatigue life of short studs embedded in ECC. • Equations to predict cyclic load-slip behavior of studs embedded in ECC were proposed. Abstract Engineered Cementitious Composites (ECC) is a family of high-performance fiber-reinforced cementitious composites with high tensile ductility and emerging in steel-concrete composite structures. In the composite structures, shear force transfer through shear connectors plays a critical role to integrate the steel and concrete. This study experimentally investigates the static and fatigue behaviors of short studs embedded in ECC through push-out tests of nine specimens. The test results of the shear strength, slip capacity, and fatigue resistance are compared with current design codes. The shear strength agrees well with that obtained from AASHTO LRFD, while the Eurocode 4 and Chinese code underestimate the shear strength. The slip capacity satisfies the ductility requirement in Eurocode 4. Based on the fatigue tests results, an S - N curve related to a survival probability of 95% is proposed. Empirical formulas are derived as a tool to help predict the load-slip behavior of the specimen under fatigue loading. [ABSTRACT FROM AUTHOR]

A high-performance fiber-reinforced cementitious composite (HPFRCC) prepared with high-volume fly ash is proposed to repair damaged reinforced concrete (RC) columns. This study aims at developing an effective and easy-to-apply repairing technique for RC columns damaged in earthquake. Four columns with 200 mm × 200 mm cross section and 900 mm height were prepared and tested to 85% of the load-carrying capacity under amplitude-increasing lateral loads and a constant axial load. The damaged columns were repaired using the HPFRCC: two repair heights (300 and 500 mm) and two repairing processes (with and without axial loads). The effectiveness of the repairing schemes was evaluated by comparing load-carrying capacities, displacement ductility, stiffness, and energy dissipation of the columns. The results indicated that the load-carrying capacity and ductility of the repaired columns could be respectively 14% and 29% higher than those of the original columns. With axial loads during repairing, the repaired columns displayed better cyclic performance. Increasing the repair height beyond the plastic hinge zone slightly improved the load-carrying capacity and ductility. Considering the performance-to-cost ratio, it is recommended that the repair height of HPFRCC be 1.5 times the depth or width of the damaged column. [ABSTRACT FROM AUTHOR]

Engineered cementitious composite (ECC), also known as strain-hardening cementitious composite (SHCC), is attracting increasing interest in the construction industry because ECC has unique mechanical properties, crack resistance, damage tolerance, and durability. The properties of ECC have originated from the unique design philosophy for tuning the cementitious matrix, fibers, and fiber-matrix interface. To facilitate the applications of ECC in the construction industry, this paper aims at clarifying the specification, characterization, and quality control methods for ECC materials. The specification includes raw materials, processing and production methods, and key properties such as the fresh and hardened properties as well as durability. A two-stage quality control approach is presented to elucidate the quality control efforts before and during construction events. The representative ranges of ECC properties are introduced, and the methods for evaluating ECC properties and representative applications are reviewed. This paper provides guidelines for practicing engineers and facilitates the applications of ECC in the construction industry. • The specification, characterization, and quality control methods for ECC materials are reviewed. • The specification includes raw materials, processing and production methods, and key properties. • A two-stage quality control approach is presented for the construction industry. • The processing and production methods largely impact the fresh and hardened properties of ECC. • This review provides guidelines to practicing engineers for applications of ECC in construction. [ABSTRACT FROM AUTHOR]

High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R2 > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks. [Display omitted] • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μ m and a R2 value of 0.984. [ABSTRACT FROM AUTHOR]

• An innovative orthotropic composite deck is proposed to achieve desired fatigue behaviors. • Engineered cementitious composite (ECC) is employed as a high-performance overlay. • The innovative orthotropic composite deck integrates conventional steel deck and ECC. • Full-scale orthotropic composite decks are experimentally tested under fatigue loading. • The innovative orthotropic composite deck exhibits desired fatigue resistance and robustness. Orthotropic steel decks offer many advantages in bridges, but they are prone to fatigue damage. One of the effective approaches to increase the fatigue resistance is to enhance the stiffness through applying a concrete layer, forming a composite section with the steel deck. However, once the concrete is cracked, the composite action is compromised. To improve the fatigue resistance, this study proposes a composite deck using engineered cementitious composite (ECC) and large U-ribs through experimentation and simulations. Two full-scale composite decks were tested to investigate the fatigue resistance and failure process, and validate a finite element model that was used to elucidate the effect of ECC on the fatigue performance. The test results showed that the composite deck had sufficient fatigue resistance, and the analysis results showed that the ECC overlay reduced the stress range by 90% at the rib-to-deck and diaphragm welded joints and 54% at the rib-to-diaphragm welded joints. One interesting finding is that the proposed deck has a robust fatigue resistance even after damages were caused in the deck system. This study is expected to advance the knowledge of the effect of ECC on the fatigue resistance, understand the underlying mechanisms, and promote further research and potential applications of the proposed composite deck. [ABSTRACT FROM AUTHOR]

Highlights • Mixture proportioning variables affect tri-axial compressive properties of HPFRCC. • Tri-axial compressive strength of HPFRCC degrades after exposure to high temperature. • Tri-axial compressive strain limit increases with heating temperature from 600 °C to 800 °C. • Tri-axial compressive strength approximately linearly increases with lateral pressure. • Tri-axial compressive strain limit approximately linearly increases with lateral pressure. Abstract This study experimentally investigates tri-axial compressive properties of high-performance fiber-reinforced cementitious composites after exposure to high temperatures up to 800 °C. First, 16 mixtures were designed and tested to evaluate the effect of mixture proportioning variables on the tri-axial compressive properties. The investigated mixture proportioning variables included the water-to-binder ratio (0.24–0.36), sand-to-binder ratio (0.36–0.66), fly ash content (60–75%), fiber content (1.5–2.2%), and superplasticizer content (0.10–0.25%). Based on the test results, an optimum mixture was presented and tested at five confining pressure levels up to 25 MPa and three loading rates up to 60 με/s before and after exposure to high temperature. The cylinders were exposed to the targeted temperatures and then loaded under combined constant lateral confining pressure and an increasing longitudinal compressive load until failure. The tri-axial compressive strength degraded as the heating temperature increased from 200 °C to 800 °C; the strain limit increased with the heating temperature from 600 °C to 800 °C. As the confining pressure increased from 5 MPa to 25 MPa, the tri-axial compressive strength increased by about 50% at 25 °C and 80% at 800 °C. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent photo interpretation approach to automatically monitor and characterize dense interconnected microcracks in strain-hardening cementitious composite (SHCC) featuring unique crack patterns in terms of crack number and crack width. The presented approach employs a stereo vision system that integrates binocular and monocular cameras for automatic detection, ranging, and quantification of cracks as well as characterization of crack patterns. The presented approach was implemented into evaluation of SHCC in flexural tests and direct tension tests. Dense microcracks were detected and ranged by the stereo vision system, segmented by an encoder-decoder approach, and quantified by an efficient computer vision approach. Evolution of the cracks was traced throughout the loading process until failure, and a statistical analysis revealed that the crack width was retained while the crack number monotonically increased. The interpretation time was shorter than 0.4 s for each photo, making the approach promising for monitoring of SHCC. The proposed system can be deployed for automated assessment of cementitious composites with complex crack patterns in material research and engineering structures. • Complex cracks in strain-hardening cementitious composites are intelligently characterized. • Ranging is incorporated to enable automatic photo interpretation without human intervention. • Real-time assessment of crack width and crack number is achieved. • The proposed approach showed high accuracy in crack detection and quantification. [ABSTRACT FROM AUTHOR]

Reconfigurable modular structures are able to be assembled using prefabricated modules and reconfigured to promote automated construction and to improve sustainability and resilience of infrastructure, while the computer-aided design and modeling of the modules are unclear. This study develops a many-objective optimization approach to design the modules made using strain-hardening cementitious composite. The proposed approach integrates a sequential surrogate model, Latin hypercube sampling method, Unified Non-dominated Sorting Genetic Algorithm III, and Technique for Order of Preference by Similarity to Ideal Solution to predict and optimize the properties of assemblages of the modules. Four objective functions were defined using the load-carrying capacity, deformability, stiffness, and volume. Results showed that the proposed method had reasonable prediction accuracy. The optimal design increased the load-carrying capacity, deformability, and stiffness by 22.8%, 11.5%, and 129.2%, respectively, and reduced the volume by 51.6%. This study is expected to effectively improve the design of reconfigurable modular structures. • Innovative concrete modules are designed and optimized for reconfigurable modular structures. • An engineer-friendly framework is presented for many-objective optimization of the modules. • The load capacity, stiffness, deflection, and volume of the modules are optimized. • A sequential surrogate model method is developed to predict the mechanical behaviors. • The UNSGA-III algorithm is utilized for many-objective optimization of the modules. [ABSTRACT FROM AUTHOR]

• Full-scale reinforced concrete beams were damaged under combined fire and mechanical loads. • Strain-hardening cementitious composite is proposed to repair fire-damaged beams. • Flexural behavior of the repaired full-scale beams is experimentally investigated. • Effects of stirrup ratio and repairing methods on the flexural behavior of beams are studied. • Analytical formulae are derived to predict load-carrying capacity of the repaired beams. This study proposes a method to repair fire-damaged concrete beams using strain-hardening cementitious composite (SHCC) and investigates their flexural behavior through full-scale tests. Eleven beams with different reinforcement ratios were fabricated. Eight beams were damaged in fire, and six of them were repaired using SHCC or basalt-fiber-reinforced polymer fabric. Three beams were used as control and not exposed to fire. All the beams were tested under four-point bending until failure. The results indicated that the proposed repairing method was capable of increasing the load-carrying capacity, stiffness, and crack resistance of fire-damaged beams. Repairing the side and bottom faces of the fire-damaged beams using SHCC increased the load-carrying capacity by 32%. To promote evaluation and repair of fire-damaged beams in engineering practices, a mechanical analysis was performed to derive engineer-friendly formulae for predicting the load-carrying capacity of repaired beams and validated against test data. This study will promote repair and evaluation of fire-damaged structures using SHCC. [ABSTRACT FROM AUTHOR]

To upcycle waste concrete through producing strain-hardening cementitious composites (SHCC) for sustainable and resilient structures, this research investigates the effect of fine recycled concrete powder on the fresh and hardened properties, optimizes the mixture design, evaluates the structural behavior, and assesses the life-cycle performance of SHCC at material and structural levels. The investigated fresh and hardened properties include the flowability, compressive strength, tensile strength, and ductility. Four full-scale beams, including a conventional reinforced concrete beam and three reinforced SHCC beams with the optimal SHCC mixture, were tested under flexural loads until failure. Life-cycle performance of SHCC was assessed regarding the cost and carbon footprint. The results showed that appropriate use of recycled concrete powder increased the ductility while retaining the adequate compressive and tensile strengths as well as flowability of SHCC. The use of the developed SHCC with recycled concrete powder increased the load capacity and crack resistance of the full-scale reinforced beams. The developed SHCC involves higher upfront cost and carbon footprint but lower life-cycle cost and carbon footprint for the investigated structural beam applications when the service life is longer than 40 years. • High-volume waste concrete is upcycled via producing eco-friendly SHCC. • Waste concrete fine powder is used to replace cement in preparing SHCC. • Mixture design of SHCC incorporating waste concrete powder is optimized. • Materials properties of SHCC and flexural properties of SHCC reinforced beams are tested. • Life-cycle assessment of SHCC and beams with waste concrete powder is performed. [ABSTRACT FROM AUTHOR]

This study develops a framework for property prediction and multi-objective optimization of strain-hardening cementitious composites (SHCC) based on automated machine learning. Three machine learning models are developed to predict the compressive strength, tensile strength, and ductility of SHCC. A tree-based pipeline optimization method is enhanced and used to enable automatic configuration of machine learning models, which are trained using three datasets considering 14 mix design variables and achieve reasonable prediction accuracy. With the predictive models, five objective functions are formulated for mechanical properties, life-cycle cost, and carbon footprint of SHCC, and the five objective functions are optimized in six design scenarios. The objective functions are optimized using innovative optimization and decision-making techniques (Unified Non-dominated Sorting Genetic Algorithm III and Technique for Order of Preference by Similarity to Ideal Solution). This research will promote efficient development and applications of high-performance SHCC in concrete and construction industry. • A new framework for prediction and multi-objective optimization of SHCC is developed. • An automated machine learning method is developed to predict key mechanical properties. • The predicted properties are the compressive strength, tensile strength, and ductility. • Six multi-objective scenarios are proposed for design of SHCC in different applications. • The mechanical properties, carbon footprint, and cost are considered for design of SHCC. [ABSTRACT FROM AUTHOR]

• Large-scale joints are assembled using prefabricated beams and columns with ECC. • Assembled joint specimens achieve comparable cyclic behavior with cast-in-place specimen. • ECC greatly enhances the load-carrying capacity and ductility of the joint specimens. • Effects of bar splicer sleeves and connection position on the cyclic behavior are investigated. This paper studies cyclic behaviors of joints assembled by prefabricated beams and columns made using Engineered Cementitious Composite (ECC). Five large-scale joint specimens were prepared and tested under cyclic loads until failure, including two joints made using conventional concrete and three joints made using ECC. One specimen was monolithically fabricated and used as the control. Three different assembling schemes were investigated to test the effects of bar splicer sleeves and connection position on the load-carrying capacity, failure mode, and ductility of the assembled joints. The results showed that ECC improved the load-carrying capacity and ductility of assembled joints. Employment of additional longitudinal bars and splicer sleeves enhanced the load-carrying capacity but compromised ductility because the failure mode was changed from flexural to shear failure. When ECC was used, the cyclic behavior was insensitive to connection position. This study is expected to promote design and engineering application of assembled joints. [ABSTRACT FROM AUTHOR]

• Impacts of relative humidity (RH) on tensile properties of ECC is elucidated. • Its mechanism is revealed by the response of matrix and fiber/matrix interface to RH. • It advances the standardization of ECC preparation of property characterization. In recent years, broad interests in ductile Engineered Cementitious Composites (ECC) have been accompanied by increasing amount of laboratory investigations of this material. Both wet and air curing have been applied in the preparation of specimens. However, the effect of curing condition on mechanical properties of ECC has yet to be elucidated. This research attempts to fill this knowledge gap. Specifically, experiments were conducted to investigate property changes at different length scales under three curing relative humidity (RH) levels. Macroscopic properties including composite first crack strength, ultimate tensile strength and strain capacity, and crack pattern of ECC reinforced with PVA fibers were recorded. As well, matrix fracture toughness and fiber/matrix interface properties were measured. Correlation of macro-properties and micromechanical parameters was interpreted using a previously developed micromechanical model. The changes of tensile properties and crack pattern were found traceable to RH effects on the matrix and fiber/matrix interface properties. The findings of this study reveal the underlying mechanisms of property differences in specimens cured under different RH. The knowledge gained provides a better understanding of the effects of curing conditions on ECC specimens used in property characterization, and is particularly relevant to maintaining consistency in standardized testing of ECC. [ABSTRACT FROM AUTHOR]

In order to investigate the cracking behavior of curved steel-concrete composite mechanical behavior under a hogging moment, two composite box girders with a central angle of 9° were designed and tested under static loads. In the reported test program, the CCB-1 was designed with steel fiber reinforced concrete (SFRC) slab and shear studs. In contrast, the CCB-2 was designed with Engineered Cementitious Composites (ECC) and Uplift-Restricted and Slip-Permitted (URSP) connectors for enhanced crack resistance. The load-displacement curve, strength and displacement ductility, failure mode, and strain distribution were reported in detail. For the tests of small curvature beams loaded under the hogging moment, the flexural critical failure mode was observed for both specimens, which was governed by compressive yielding of the top steel plate and tensile yielding of the concrete slab. The URSP connectors in CCB-2 effectively released the interface slip of composite girders, enhanced the interface slip capacity, and reduced the crack width of concrete compared with traditional shear studs in CCB-1. Besides, the nonlinear elaborate finite element (FE) models of two specimens were developed, and the modeling scheme and the material constitutive model were reported in detail. The developed nonlinear FE model well predicted the test results in terms of load-displacement curve, initial stiffness, failure mode, and strain distribution of concrete slab and steel beam. Both test results and FE results showed that the shear lag behavior of the concrete slab was insignificant, while the steel flange plates showed a notable shear lag effect. Therefore, the performance of the URSP connectors is well validated. • Two composite box girders were tested to investigate crack resistance of ECC and URSP connector. • The failure mode, load-displacement relationship, shear lag and distortion behavior were investigated. • The ECC material notably reduce the crack width of composite beam in hogging moment regions. • The URSP connectors effectively reduce the crack width and crack strain in composite beams. • The non-linear finite element model was developed and well simulated the global and local test behavior. [ABSTRACT FROM AUTHOR]