The sorption curve is an essential feature for the modelling of heat and mass transfer in porous building materials. Several models have been proposed in the literature to represent the amount of moisture content in the material according to the water activity (or capillary pressure) level. These models are based on analytical expressions and few parameters that need to be estimated by inverse analysis. This article investigates the reliability of eight models through the accuracy of the estimated parameters. For this, experimental data for a wood fibre material are generated with special attention to the stop criterion to capture long time kinetic constants. Among five sets of measurements, the best estimate is computed. The reliability of the models is then discussed. After proving the theoretical identifiability of the unknown parameters for each model, the primary identifiability is analysed. It evaluates whether the parameters influence on the model output is sufficient to proceed the parameter estimation with accuracy. For this, a continuous derivative-based approach is adopted. Seven models have a low primary identifiability for at least one parameter. Indeed, when estimating the unknown parameters using the experimental observations, the parameters with low primary identifiability exhibit large uncertainties. Finally, an Approximation Bayesian Computation algorithm is used to simultaneously select the best model and estimate the parameters that best represent the experimental data. The GAB and Fredlund-Xing models, together with a proposed model in this work, were the best ones selected by this algorithm.

中文翻译：

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木纤维材料吸水参数估计与模型选择

吸附曲线是多孔建筑材料传热传质建模的基本特征。文献中提出了几种模型来根据水分活度（或毛细管压力）水平表示材料中的水分含量。这些模型基于解析表达式和少量需要通过逆分析估计的参数。本文通过估计参数的准确性来考察八种模型的可靠性。为此，生成了木纤维材料的实验数据，特别注意停止标准以捕获长时间的动力学常数。在五组测量中，计算最佳估计。然后讨论模型的可靠性。在证明了每个模型未知参数的理论可识别性后，分析了主要的可识别性。它评估参数对模型输出的影响是否足以准确地进行参数估计。为此，采用了基于连续导数的方法。七个模型对至少一个参数的主要可识别性较低。事实上，当使用实验观察估计未知参数时，具有低初始可识别性的参数表现出很大的不确定性。最后，使用近似贝叶斯计算算法同时选择最佳模型并估计最能代表实验数据的参数。GAB 和 Fredlund-Xing 模型以及本工作中提出的模型是该算法选择的最佳模型。