Construction and demolition (C&D) waste materials have been used in a wide range of civil engineering applications, particularly as unbound pavement materials. A comprehensive understanding of the deformation and thermal properties of C&D materials is necessary for their usage in novel applications related to heat transfer in pavement unbound layers, such as geothermal pavements. This research study focused on developing a correlation between the resilient modulus (M_R) and thermal conductivity of C&D materials for geothermal pavement applications. The thermal conductivity of C&D materials, namely recycled concrete aggregate (RCA), crushed brick (CB), reclaimed asphalt pavement (RAP), and waste rock (WR), was evaluated at different moisture contents and dry densities. The M_R and permanent deformation responses of C&D materials were characterized at the optimum moisture content (OMC), 85%OMC, and 70%OMC, using the repeated load triaxial (RLT) test. An intelligent model was developed for predicting the M_R of C&D materials incorporating thermal conductivity, physical properties, confining stress, and deviator stress as input parameters using adaptive neuro-fuzzy inference system (ANFIS) approach. The developed ANFIS model had excellent performance in predicting the M_R of C&D materials, with R² = 0.99 for both training and testing datasets. The ANFIS model was converted into a mathematical relationship, which can be used by researchers and practitioners for estimating the M_R of C&D materials.

建筑和拆除 (C&D) 废料已用于广泛的土木工程应用，特别是作为未结合的路面材料。全面了解 C&D 材料的变形和热性能对于将其用于与路面未结合层（例如地热路面）中的传热相关的新应用是必要的。此研究集中在开发弹性模量（M之间的相关性_[R ）和C＆d材料地热路面应用中的热导率。C&D 材料，即再生混凝土骨料 (RCA)、碎砖 (CB)、再生沥青路面 (RAP) 和废石 (WR) 的导热系数在不同的水分含量和干密度下进行了评估。所述M _ř使用重复载荷三轴 (RLT) 测试在最佳含水量 (OMC)、85%OMC 和 70%OMC 下表征 C&D 材料的永久变形响应。开发了一种智能模型，用于使用自适应神经模糊推理系统 (ANFIS) 方法预测C&D 材料的 M _R，其中包括热导率、物理特性、围压和偏应力作为输入参数。开发的 ANFIS 模型在预测C&D 材料的 M _R方面具有出色的性能， 训练和测试数据集的R ² = 0.99。ANFIS 模型被转换为数学关系，可供研究人员和从业人员用于估计C&D 材料的 M _R。