A material-tailored special concrete composite that uses a synthetic fiber to make the concrete ductile and imposes strain-hardening characteristics with eco-friendly ingredients is known as an “engineered geopolymer composite (EGC)”. Mix design of special concrete is always tedious, particularly without standards. Researchers used several artificial intelligence tools to analyze and design the special concrete. This paper attempts to design the material EGC through an artificial neural network with a cross-validation technique to achieve the desired compressive and tensile strength. A database was formulated with seven mix-design influencing factors collected from the literature. The five best artificial neural network (ANN) models were trained and analyzed. A gradient descent momentum and adaptive learning rate backpropagation (GDX)–based ANN was developed to cross-validate those five best models. Upon regression analysis, ANN [2:16:16:7] model performed best, with 74% accuracy, whereas ANN [2:16:25:7] performed best in cross-validation, with 80% accuracy. The best individual outputs were “tacked-together” from the best five ANN models and were also analyzed, achieving accuracy up to 88%. It is suggested that when these seven mix-design influencing factors are involved, then ANN [2:16:25:7] can be used to predict the mix which can be cross-verified with GDX-ANN [7:14:2] to ensure accuracy and, due to the few mix trials required, help design the SHGC with lower costs, less time, and fewer materials.

In recent years, immense development in Structural Health Monitoring (SHM) of bridges helps address the life span and reliability of bridge structure at contrasting phases of their service life. This article provides a detailed understanding of bridge monitoring, and it focuses on sensors utilized and all kinds of damage detection (strain, displacement, acceleration, and temperature) according to bridge nature (scour, suspender failure, disconnection of bolt and cables, etc.) and environmental degradation under static and dynamic loading. This paper presents information about various methods, approaches, case studies, advanced technologies, real‐time experiments, stimulated models, data acquisition, and predictive analysis. Future scope and research also discussed the implementation of SHM in bridges. The main aim of this research is to assist researchers in better understanding the monitoring mechanism in bridges.

Flexural Response of Fibre Reinforced Polymer Laminated Pre-stressed Concrete Beams

In this research study, the performance of geopolymer concrete (GPC) in-filled fibre-reinforced polymer (FRP) composite (GPC-FRP) columns exposed to compressive loading is examined using the finite element (FE) analysis. The load–deflection behaviour is investigated by considering the impact of the strength of concrete, different fibre orientations and thicknesses of FRP tubes in terms of the diameter/thickness (D/t) ratio, surface friction in between the concrete and enclosing FRP tube, the lateral confinement and the axial stress distribution characteristics. The load-carrying capacity (LCC) of the GPC-FRP composite columns and cement concrete (CC) in-filled FRP composite (CC-FRP) columns is compared and the results imply that the LCC of the GPC-FRP composite columns is (0.9 to 2.04%) greater than the CC-FRP composite columns. The improvement in the LCC and lateral confining pressure of the GPC-FRP composite columns is observed as the thickness of the FRP tube increases. The LCC of the GPC-FRP composite columns with a D/t ratio of 30 was almost (12.70 to 14.23%) greater than the GPC-FRP composite columns with a D/t ratio of 50. The GPC-FRP composite columns with a fibre orientation in the axial and hoop directions (0°) exhibit (8.4 to 11.39%) better performance than the columns with any other orientations (30° and 53°). The LCC of the GPC-FRP composite columns with a coefficient of friction of 0.25 and 0.5 are quite comparable. The axial stress distribution in the GPC-FRP composite columns with different tube thicknesses is explored in this research. This FE model is validated with the experimental results obtained by Kim et al., (2015) and the load and deflection are predicted with the validation error of 6.5 and 6.1%, respectively.