The flame retardancy of asphalt binders with layered double hydroxides (LDHs) was investigated using limiting oxygen index (LOI) and cone calorimeter tests. The flame-retardant mechanism of the LDHs was also studied with thermogravimetry and differential scanning calorimetry (TG–DSC), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The cone calorimeter testing results indicated that 2 wt.% of the LDHs can decease the peak heat and smoke release rate of asphalt binders. Because a low dose of LDHs can be well dispersed in asphalt binder and favor the formation of polyaromatic structures during combustion, the thermal oxidation resistance and compactness of the char layer can be improved. The LOI of asphalt binder can be increased and the heat and smoke release during combustion can be decreased with 25 wt.% LDHs. The decomposition of LDHs can absorb the heat release of the initial two stages of asphalt combustion and reduce the burning rate of asphalt. Due to the loss of loosely bound water in the LDHs during the blending process and the decrease of dispersibility at a high LDH dose, the improvement of thermal stability is limited.

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In this study, ultrasonic pulse velocity (UPV) and ultrasonic shear-wave tomography are combined to measure the residual compressive strength (RCS) of small-scale lining concrete blocks and to detect inner defects in the lining structure. The characteristics of and variations in the RCS of test blocks after being exposed to elevated temperatures (200–800 °C) and constant heating times (2 h, 3 h, and 4 h) were studied. At 800 °C, the RCS values reduced by 64.4%, 69.2%, and 74.6% at heating times of 2 h, 3 h, and 4 h. Scanning electron microscopy (SEM) was used for the micro-phase analysis of the samples that had been exposed to high temperatures. The heating time and RCS as well as the SEM micro-structure relationship were compared. Finally, a tunnel lining slab sample was designed to simulate the post-fire damage inside the blocks. Additionally, shear-wave tomography with 32 probes was able to detect the ϕ10 mm void defects at a depth of 200 mm.

Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNIe software.