A computer-implemented method comprising: - a first step of clustering pipe sections of a pipe network into a number of classes, based on pipe parameters relative to the structure or to the environment of the pipe sections; - and, for each class of said number of classes: - a second step of extracting a sample of pipe sections of the class, said second step comprising: ▪ a first sub-step of initializing a set of candidate samples of pipe sections; ▪ a second sub-step of iteratively modifying said set of candidate samples using: • a genetic algorithm based on an objective function comprising a minimization of the difference of average pipe parameters of the pipe sections of the sample, and • the average pipe parameters of the pipe sections of the class; ▪ a third sub-step of selecting the candidate sample that optimizes said objective function; - a third step of obtaining, for each pipe section sample, one or more pipe condition scores determined by a condition assessment procedure; - a fourth step of performing an estimation of one or more pipe condition scores for pipe sections that do not belong to the sample based on said pipe parameters, said estimation being parameterized with the pipe condition scores and pipe parameters of the pipe sections of the sample extracted at the second step. - a first step of clustering pipe sections of a pipe network into a number of classes, based on pipe parameters relative to the structure or to the environment of the pipe sections; - and, for each class of said number of classes: - a second step of extracting a sample of pipe sections of the class, said second step comprising: ▪ a first sub-step of initializing a set of candidate samples of pipe sections; ▪ a second sub-step of iteratively modifying said set of candidate samples using: • a genetic algorithm based on an objective function comprising a minimization of the difference of average pipe parameters of the pipe sections of the sample, and • the average pipe parameters of the pipe sections of the class; ▪ a third sub-step of selecting the candidate sample that optimizes said objective function; - a third step of obtaining, for each pipe section sample, one or more pipe condition scores determined by a condition assessment procedure; - a fourth step of performing an estimation of one or more pipe condition scores for pipe sections that do not belong to the sample based on said pipe parameters, said estimation being parameterized with the pipe condition scores and pipe parameters of the pipe sections of the sample extracted at the second step. - a second step of extracting a sample of pipe sections of the class, said second step comprising: ▪ a first sub-step of initializing a set of candidate samples of pipe sections; ▪ a second sub-step of iteratively modifying said set of candidate samples using: • a genetic algorithm based on an objective function comprising a minimization of the difference of average pipe parameters of the pipe sections of the sample, and • the average pipe parameters of the pipe sections of the class; ▪ a third sub-step of selecting the candidate sample that optimizes said objective function; ▪ a first sub-step of initializing a set of candidate samples of pipe sections; ▪ a second sub-step of iteratively modifying said set of candidate samples using: • a genetic algorithm based on an objective function comprising a minimization of the difference of average pipe parameters of the pipe sections of the sample, and • the average pipe parameters of the pipe sections of the class; • a genetic algorithm based on an objective function comprising a minimization of the difference of average pipe parameters of the pipe sections of the sample, and • the average pipe parameters of the pipe sections of the class; ▪ a third sub-step of selecting the candidate sample that optimizes said objective function; - a third step of obtaining, for each pipe section sample, one or more pipe condition scores determined by a condition assessment procedure; - a fourth step of performing an estimation of one or more pipe condition scores for pipe sections that do not belong to the sample based on said pipe parameters, said estimation being parameterized with the pipe condition scores and pipe parameters of the pipe sections of the sample extracted at the second step.