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.

Environmental noise pollution is the primary environmental problem faced by the superstructure of metro depots. The throat area of depot is prone to high wheel-rail impact noise due to the use of seam lines, multiple joints, turnouts and small radius curves. The noise exerts through fire and ventilation openings on the side walls, which may cause high annoyance to the residents in the superstructures both on the upper cover and surrounding areas. In this paper, a field test was condected on the environmental noise in the throat area of metro depot. The noise of the trackside and adjacent open space were recorded, and the time-frequency domain characteristics and statistical characteristics and attenuation law of the noise generated by train operation in the throat area were analyzed. The research results have certain guiding significance for the prediction and control of noise in the throat area of the depot.

To investigate the relationship between the structure-borne noise and the vibration of vertical concrete noise barriers sitting on the roadside of the railway, the time-frequency characteristics of the accelerations and sound pressure level of the barrier slab under train crossing events were analyzed by in situ vibration and noise testing. The results show that (1) the vertical vibration level in the mainline area is approximately 94.7 dB, while it is approximately 104.4 dB in the turnout area, and the average increase effect of the vibration level is approximately 9.7 dB. (2) The sound pressure level of the structure-borne noise in the mainline area is approximately 66.4 dB(A), while it is approximately 70.0 dB(A) in the turnout area, and the average increase effect of the sound pressure level is approximately 3.6 dB(A). (3) The vibration is amplified significantly at approximately 40-60 Hz. In summary, the structure-borne noise of the barrier slab is affected by the forced vibration amplification of the barrier slab, and the sound and vertical vibration amplified analysis of the noise barrier under ground motion excitation caused by train operation brought out the related relationship between the vertical vibration and the structure-borne noise.