In this study, the morphologies of the aggregate in multiple views were analysed during the falling of particles to calculate aggregate gradation. Four types of characterisation parameters were selected to extract the multi-view information of aggregate particles in five views. Based on the multi-view information, the aggregate particles were classified using principal component analysis and a probabilistic neural network. An aggregate equivalent volume characterisation method was formulated to calculate the aggregate mass, whereby the aggregate volume was converted into the aggregate mass by the least-squares method. The experimental results show that the proposed aggregate sieving method can effectively realise the gradation classification of aggregates. Considering the product of the maximum area and the minimum equivalent Feret ellipse minor axis as the equivalent volume, the calculated aggregate mass yielded a good correlation with the actual aggregate mass. Compared with single-view information, multi-view information can improve the accuracy and repeatability of gradation calculations. The use of multi-view information to calculate aggregate gradation can reduce manpower and improve detection efficiency, which is important for applications in the construction industry. Display omitted •The morphologies of the falling aggregate were collected in multiple views.•The size information of aggregate in multiple views was fused using Principal Component Analysis.•The aggregate sieving was realized by Probabilistic Neural Network.•The characterisation method of aggregate equivalent volume was proposed.•The mapping relationship between the equivalent volume and the mass of aggregate was established by the least-squares method.