A method of monitoring a structure, comprising the following steps: a) acquiring data, the data comprising a plurality of operational parameters x i ( t j) and at least one strain data y ( t j), the operational parameters x i and the strain data y being acquired synchronously over a time series basis ( t 1, t 2,..., t m) using the same or different sampling rates; b) building a significant points dataset from the data acquired in step a); the method characterized by the following steps: c) modelling a relationship between the operational parameters x i ( t j) and the strain data y ( t j) using the built significant points dataset to train a non-adaptive prediction functional supervised approximation method, wherein the step of building a significant points dataset comprises: (b 1) for each time sample t j, j = 1,.., m, determining the data modulus ∥ μ ( t j)∥ of data vectors μ ( t j) defined as: μ ̅ t j = x 1 t j, x 2 t j,..., x n t j; y t j, the data modulus ∥ μ ( t j)∥ being calculated as: ‖ μ ̅ t j ‖ = ∑ i = 1 n x ̃ i ⁢ t j 2 + y ̃ ⁢ t j 2 with x ̃ i t j = 2 ⋅ x i t j - x i min x i max - x i min - 1, for i = 1,..., n; y ̃ t j = 2 ⋅ y t j - y min y max - y min - 1 and x i min, x i max, y min, y max being respectively the minimum and maximum values for x i and the minimum and maximum values for y in the time series ( t 1, t 2,..., t m); sorting the data vectors μ ( t j), j = 1,.., m, in N R groups according to the value of their modulus, each group including data with values of modulus within a defined range; computing within each group the angle α between pairs of vectors and when the angle between a first vector and a second vector is less than or equal to a predetermined angle θ, deleting one of the two vectors. a) acquiring data, the data comprising a plurality of operational parameters x i ( t j) and at least one strain data y ( t j), the operational parameters x i and the strain data y being acquired synchronously over a time series basis ( t 1, t 2,..., t m) using the same or different sampling rates; b) building a significant points dataset from the data acquired in step a); the method characterized by the following steps: c) modelling a relationship between the operational parameters x i ( t j) and the strain data y ( t j) using the built significant points dataset to train a non-adaptive prediction functional supervised approximation method, (b 1) for each time sample t j, j = 1,.., m, determining the data modulus ∥ μ ( t j)∥ of data vectors μ ( t j) defined as: μ ̅ t j = x 1 t j, x 2 t j,..., x n t j; y t j, the data modulus ∥ μ ( t j)∥ being calculated as: ‖ μ ̅ t j ‖ = ∑ i = 1 n x ̃ i ⁢ t j 2 + y ̃ ⁢ t j 2 with x ̃ i t j = 2 ⋅ x i t j - x i min x i max - x i min - 1, for i = 1,..., n; y ̃ t j = 2 ⋅ y t j - y min y max - y min - 1 and x i min, x i max, y min, y max being respectively the minimum and maximum values for x i and the minimum and maximum values for y in the time series ( t 1, t 2,..., t m); sorting the data vectors μ ( t j), j = 1,.., m, in N R groups according to the value of their modulus, each group including data with values of modulus within a defined range; computing within each group the angle α between pairs of vectors and when the angle between a first vector and a second vector is less than or equal to a predetermined angle θ, deleting one of the two vectors.