In this study, an artificial neural network (ANN) model was designed to predict color change based on visual assessment of coated cross laminated timber (CLT) exposed outdoors. Coatings and stains were investigated based on ASTM protocols to assess wood surface visual rating, against checking, flaking, erosion, and mildew growth in the State of Mississippi (USA) during one year (2019–2020). It was hypothesized that accurate ratings would promote precise color prediction by the ANN model. Visual assessment inputs were used to develop the model for predicting total color change (Δ E ). The training and validation splits of the network were based on a 10-fold cross-validation technique, and the ANN model performance was assessed on the validation set using mean squared error (MSE), mean average precision (MAE), and coefficient of determination ( R 2 ) after permutation feature importance analysis (PFI). Results indicated that coating was the most important feature in color change model. Erosion, checking and flaking achieved similar importance with an approximate difference of 6%. The ANN model was able to effectively predict color change values based on visual ratings with overall accuracy of 95% on truly unseen data. These findings revealed that coating properties, visual appearance, time of exposure, are associated with discoloration. Accurate visual assessment and a well-trained ANN can successfully provide the desired values of Δ E with a smaller number of complex test procedures.

中文翻译：

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基于涂层交叉层压木材 (CLT) 视觉分析的人工神经网络建模预测户外暴露期间的颜色变化

在这项研究中，人工神经网络 (ANN) 模型旨在根据暴露在户外的涂层交叉层压木材 (CLT) 的视觉评估来预测颜色变化。根据 ASTM 协议对涂层和污渍进行了调查，以评估一年（2019-2020 年）密西西比州（美国）的木材表面视觉等级，以防止检查、剥落、侵蚀和霉菌生长。假设准确的评级将促进 ANN 模型的精确颜色预测。视觉评估输入用于开发预测总颜色变化 (ΔE) 的模型。网络的训练和验证拆分基于 10 倍交叉验证技术，并使用均方误差 (MSE)、平均精度 (MAE) 在验证集上评估 ANN 模型性能，和排列特征重要性分析（PFI）后的决定系数（R 2 ）。结果表明涂层是颜色变化模型中最重要的特征。侵蚀、裂痕和剥落的重要性相似，差异约为 6%。ANN 模型能够根据视觉评级有效地预测颜色变化值，对真正看不见的数据的总体准确率为 95%。这些发现表明涂层特性、视觉外观、暴露时间与变色有关。准确的视觉评估和训练有素的人工神经网络可以通过较少数量的复杂测试程序成功提供所需的 ΔE 值。检查和剥落的重要性相似，差异约为 6%。ANN 模型能够根据视觉评级有效地预测颜色变化值，对真正看不见的数据的总体准确率为 95%。这些发现表明涂层特性、视觉外观、暴露时间与变色有关。准确的视觉评估和训练有素的人工神经网络可以通过较少数量的复杂测试程序成功提供所需的 ΔE 值。检查和剥落的重要性相似，差异约为 6%。ANN 模型能够根据视觉评级有效地预测颜色变化值，对真正看不见的数据的总体准确率为 95%。这些发现表明涂层特性、视觉外观、暴露时间与变色有关。准确的视觉评估和训练有素的人工神经网络可以通过较少数量的复杂测试程序成功提供所需的 ΔE 值。