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Smart city transportation infrastructure will soon demand the development of reliable underground IoT (IoUT) communication. In this paper, we develop a novel analytical model, MAME (Material Aware Measurement Enhanced), to capture signal propagation properties in wireless IoUT networks to achieve reliable data transport. A driving motivation is monitoring underground infrastructure systems (e.g., pipelines and storm drains) for early detection of anomalies and failures to guide human investigation and intervention. We analyze the feasibility of successfully receiving wireless data packets from underground (UG) sensor nodes through multiple material layers and under diverse environmental conditions. Our proposed approach integrates physics-based modeling and empirical studies with small-scale testbeds (in our lab and outdoors) with multiple channel setups and physical layer attributes. We derive a novel MAME approach to model signal propagation in both 802.11-based WiFi and LoRaWAN networks. The resulting MAME model is shown to capture communication behavior in WiFi and LoRaWAN networks accurately. The MAME model is used to augment the popular NS3 simulator to explore scaled-up underground networks and varying channel conditions (e.g., soil moisture level). Such a combined analytical-empirical approach will enable the communication control plane and application layer to better predict channel conditions for improved IoUT network design.