In this paper, the potential of quasi-Monte Carlo methods for uncertainty propagation is assessed, via a case study of heat loss through a massive masonry wall. Four quasi-Monte Carlo sampling strategies – Optimized Latin hypercube, Sobol sequence, Niederreiter-Xing sequence and Good Lattice sequence – are applied and compared. Moreover, in order to terminate the quasi-Monte Carlo simulation when the desired accuracy is reached, an error estimation method is implemented. The outcomes show that all the four quasi-Monte Carlo methods outperform the standard Monte Carlo method; the Niederreiter-Xing sequence and Sobol sequence tend to be the best.