In recent years, railway systems have played a significant role in transportation systems due to the demand increase in conveying both cargo and passengers. Due to the harsh environments and severe loading conditions, caused by the traffic growth, heavier axles and vehicles and increase in speed, railway tracks are at risk of degradation and failure. Condition monitoring has been widely used to support the health assessment of civil engineering structures and infrastructures. In this context, it was adopted as a powerful tool for an objective assessment of the railway track behaviour by enabling real-time data collection, inspection and detection of structural degradation. According to relevant literature, a number of sensors can be used to monitor track behaviour during the train passing under harsh environments. This paper presents a review of sensors used for structural monitoring of railway track infrastructure, as well as their application to sense the performance of different track components during extreme events. The insight into track monitoring for railways serving traffic with extreme features will not only improve the track inspection and damage detection but also enable a predictive track maintenance regime in order to assist the decision-making process towards more cost-effective management in the railway industry.

Ring foundations are widely used to support various types of offshore structures because of the cost-effective reason. Unlike the traditional circular foundations, very few studies were reported with regard to the bearing capacity of ring foundations. In addition, the study on the stability effects of anisotropic and nonhomogeneous clays is yet to be found in the current literature. The purpose of the technical note is therefore to bridge the current research gap. By using the robust lower and upper bound finite-element limit analysis in axisymmetric conditions, new stability solutions of ring footings on anisotropic and nonhomogeneous clays are obtained using dimensionless parameters. The associated failure mechanisms are also examined and discussed, while the design equations are proposed for predicting the bearing capacity of ring foundations. The findings in this study provide a reliable solution to improve the current design standard for ring footings in anisotropic and nonhomogeneous clays.

The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside with other benefits such as minimising the usage of natural resources in exploitation to produce new conventional concrete. Eventually, this will lead to reducing the construction waste, carbon footprints and energy consumption. This paper aims to study the recycled aggregate concrete compressive strength using Artificial Neural Network (ANN) which has been proven to be a powerful tool for use in predicting the mechanical properties of concrete. Three different ANN models where 1 hidden layer with 50 number of neurons, 2 hidden layers with (50 10) number of neurons and 2 hidden layers (modified activation function) with (60 3) number of neurons are constructed with the aid of Levenberg-Marquardt (LM) algorithm, trained and tested using 1030 datasets collected from related literature. The 8 input parameters such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age are used in training the ANN models. The number of hidden layers, number of neurons and type of algorithm affect the prediction accuracy. The predicted recycled aggregates compressive strength shows the compositions of the admixtures such as binders, water–cement ratio and blast furnace–fly ash ratio greatly affect the recycled aggregates mechanical properties. The results show that the compressive strength prediction of the recycled aggregate concrete is predictable with a very high accuracy using the proposed ANN-based model. The proposed ANN-based model can be used further for optimising the proportion of waste material and other ingredients for different targets of concrete compressive strength.