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The emphasis of this study is placed on the investigation into the failure mechanisms of the fabric membranes when exposed to such defective cracks. This experimental study investigates the initial crack of a flat circle and conducts a uniaxial shear test on the membrane materials. The deformation of the membrane materials is obtained through an optical non-contact scanner. Our study has been conducted to assess the crack propagation of fabric membrane materials at different angles. The relationships between crack width and stress together with stress and strain are also obtained. Based on the results, a mechanic of failure on the membrane was proposed. Moreover, new findings into the ductility and energy absorption of the fabric membrane materials have been established to inform the failure mechanisms. [ABSTRACT FROM AUTHOR]

As the key safety-critical component of a bridge support system, bridge bearings are extensively used to accommodate, balance, and transfer differential displacements and loads between the superstructure and substructure of a bridge during operations. Several studies have been conducted to obtain dynamic modal parameters of traditional bridge bearings only in perfectly dry environments. However, in extreme weather conditions (e.g., heavy rain, flash floods, etc.), water can ingress and change the bearings' properties. In this study, novel 3D-printed porous bridge bearings (3DPPBBs) have been fabricated by Fused Deposition Modeling (FDM) with thermoplastic polyurethane (TPU) filaments. This study is the first to determine the influences of flood conditions on their dynamic properties, which has never been done before. An idealised single degree of freedom (ISDOF) for these novel bearings is considered for the non-destructive field-testing technique of the critical bridge component. A series of experimental tests have been performed under several conditions of flooding levels. The new results unprecedentedly indicate that relatively higher dynamic damping ratios can be found with the increasing flood levels. In contrast, the natural frequencies and dynamic stiffness decrease with the same conditions. Novel insights are essential for bridge engineers to assess and monitor bridge vibrations exposed to extreme weather conditions. [ABSTRACT FROM AUTHOR]

This study investigates the engineering and mechanical properties of basalt fiber-reinforced (FRF) concrete, giving special attention to residual flexural strength and dynamic modal parameters. These properties, which have not been thoroughly investigated elsewhere, are a precursor to structural design applications for dynamic compliant structures (i.e., bridges, offshore platforms, railways, and airport pavement). Accordingly, the standard notched flexural tests have been carried out to assess the basalt fiber-reinforced concrete's residual flexural strength with an additional 0.125%, 0.25%, 0.375%, and 0.5% of volume fraction of basalt fiber. In addition, dynamic modal tests were then conducted to determine the dynamic modulus of elasticity (MOE) and damping of the FRF concrete beams. The results indicate that concrete's toughness and crack resistance performance are significantly improved with added fiber in basalt fiber reinforced concrete, and the optimum fiber content is 0.25%. It also exhibits the highest increment of compressive strength of 4.48% and a dynamic MOE of 13.83%. New insights reveal that although the residual flexural performance gradually improved with the addition of basalt fiber, the damping ratio had an insignificant change. [ABSTRACT FROM AUTHOR]

Bacterial-based self-healing concrete (BSHC) is a well-known healing technology which has been investigated for a few decades for its excellent crack healing capacity. Nevertheless, considered as costly and time-consuming, the healing performance (HP) of concrete with various types of bacteria can be designed and evaluated only in laboratory environments. Employing machine learning (ML) models for predicting the HP of BSHC is inspired by practical applications using concrete mechanical properties. The HP of BSHC can be predicted to save the time and cost of laboratory tests, bacteria selection and healing mechanisms adoption. In this paper, three types of BSHC, including ureolytic bacterial healing concrete (UBHC), aerobic bacterial healing concrete (ABHC) and nitrifying bacterial healing concrete (NBHC), and ML models with five kinds of algorithms consisting of the support vector regression (SVR), decision tree regression (DTR), deep neural network (DNN), gradient boosting regression (GBR) and random forest (RF) are established. Most importantly, 22 influencing factors are first employed as variables in the ML models to predict the HP of BSHC. A total of 797 sets of BSHC tests available in the open literature between 2000 and 2021 are collected to verify the ML models. The grid search algorithm (GSA) is also utilised for tuning parameters of the algorithms. Moreover, the coefficient of determination (R2) and root mean square error (RMSE) are applied to evaluate the prediction ability, including the prediction performance and accuracy of the ML models. The results exhibit that the GBR model has better prediction ability (R2GBR = 0.956, RMSEGBR = 6.756%) than other ML models. Finally, the influence of the variables on the HP is investigated by employing the sensitivity analysis in the GBR model. [ABSTRACT FROM AUTHOR]

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world's first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved. [ABSTRACT FROM AUTHOR]

Cracks could attenuate the service life of concrete structures because of the intrusion of hazardous substances such as water. In this study, different proportions of Duras S500 fibre were employed to investigate the self-sealing capacity of environmentally friendly, highly damped, fibre-reinforced concrete (EFHDFRC) containing 5% crumb rubber. The workability of EFHDFRC with different proportions of the fibre was investigated by mechanical properties test. The self-sealing capacity was first measured by introducing the ultrasonic pulse velocity (UPV) test combined with the damage degree in a time-dependent manner. In addition, the regained compressive strength test and visual inspection were applied as additional measures of the self-sealing capacity. The experimental results show that EFHDFRC with different proportions of fibre showed the maximum sealing degree between the 42nd and 51st days after casting the concrete. EFHDFRC with 0.1% fibre had the best performance and the maximum self-sealing degree (2.82%). In summary, it has been proven that 0.1% fibre could stimulate the self-sealing capacity of EFHDFRC by bridging cracked concrete. Moreover, it is noted that sufficient space in cracks is essential for precipitation formation, which could seal the cracks. The new insights of this innovative self-healing, high-damping material are essential for industrial applications exposed to dynamic load conditions such as railway turnout bearers and sleepers, highspeed rail track slabs, blast-resistant walls and columns, and so on. [ABSTRACT FROM AUTHOR]

There is no doubt that the use of waste rubber in concrete applications is a genius alternative because Styrene is the main component of rubber, which has a strong toxicity and is harmful to humans. Therefore, it will significantly reduce impacts on the environment when waste rubber can be recycled for genuine uses. In this paper, the dynamic properties of high-strength rubberised concrete have been investigated by carrying out various experiments to retain the compressive strength, tensile strength, flexural strength, electrical resistivity, and damping characteristics by replacing fine aggregates with micro-scale crumb rubber. Over 20 variations of concrete mixes have been performed. The experimental results confirm that a decrease in the compressive strength can be expected when the rubber content is increased. The new findings demonstrate that the high-strength concrete can be enhanced by optimal rubber particles in order to improve splitting tensile and flexural strengths, damping properties, and electrical resistivity. It is therefore recommended to consider the use of rubberised concrete (up to 10 wt. % crumb rubber) in designing railway sleepers as this will improve the service life of railway track systems and reduce wastes to the environment. [ABSTRACT FROM AUTHOR]