Electrical resistance strain gauges are widely used in asymmetric structures for measurement and monitoring, but their thermal output in changing temperature environments has a significant impact on the measurement results. Since thermal output is related to the coefficient of thermal expansion of the strain gauge’s sensitive grating material and the measured object, the temperature self-compensation technique of strain gauges fails to eliminate the additional strain caused by temperature because it cannot match the coefficient of thermal expansion of various measured objects. To address this problem, in this study, the principle of the thermal output of electrical resistance strain gauges was analyzed, a calibration experiment for thermal output in the case of a mismatch between the coefficient of linear expansion of the measured object and the strain gauge grating material was conducted, and the mechanism for temperature influence on thermal output was revealed. A method was proposed to obtain the thermal output curves for different materials by using thermostats with dual temperatures to conduct temperature calibration experiments. A linear regression method was used to obtain a linear formula for the thermal output corresponding to each temperature. The thermal output conversion relationship was derived for materials with different coefficients of linear expansion. An in situ temperature compensation technique for electrical resistance strain gauges that separates the measured strain into thermal and mechanical strains was proposed. The results showed that the thermal output curve for the measured object can be calibrated in advance and then deducted from the measured strain, thus reducing the influence of temperature-induced additional strain on the mechanical strain. In addition, a new method was provided for the calculation of the thermal output among materials with similar coefficients of linear expansion, providing a reference for the health monitoring of asymmetric structures.

Microparameter calibration is an important problem that must be solved in the discrete element method. The Gaussian process (GP) response surface methodology was proposed to calibrate the microparameters based on the Bayesian principle in machine-learning methods, which addresses the problems of uncertainty, blindness, and repeatability in microparameter calibration methods. Using the particle flow code (PFC) as an example, the effects of the microparameters on the macroparameters were evaluated using the control-variable method, and the range of the microparameters was determined based on the macroparameters. The uniform design (UD) method and numerical calculation were used to obtain training samples, and a GP response surface methodology suitable for multifactor, multilevel, and nonlinear processes was used to establish the response surface relationships for macro–micro parameters of rock-like materials in discrete element method. According to the macroparameters obtained from the uniaxial experiments conducted on rock specimens, the microparameters were calibrated using the GP response surfaces. Numerical calculations of uniaxial compression and Brazilian splitting were performed using microparameters, and the results were compared with laboratory experiments for verification. The results showed that the relative errors of the GP response surface and laboratory test values were 5.3% for the modulus of elasticity, −7.8% for compressive strength, and −2.6% for tensile strength. The nonlinear GP response surface considered the characteristics of multiple interacting factors, and the established nonlinear response surface relationship between the microparameters and macroparameters can be used for the calibration of microparameters. The accuracy of the microparameters was verified according to the stress–strain curve and failure morphology of the rock specimens. The method of using the GP response surface to establish the macro–micro parameter relationship in the discrete element method can also be extended to other numerical simulation methods and can provide a basis for accurately analysing the microdamage mechanism of rock materials under complex loading conditions.

The failure characteristics of coal rock and the energy dissipation mechanisms during stress wave propagation under impact loading exhibit significant differences compared to conventional rock. In this paper, short coal samples (50 mm in length) and long coal samples (360 mm in length) were prepared to conduct multi-strain-rate impact tests utilizing a split Hopkinson pressure bar (SHPB) test system. A high-speed digital camera was employed to capture the crack propagation and fracture process of the coal samples, while fractal dimension analysis was applied to quantify the distribution characteristics of fragment sizes. During impact tests on long coal samples, strain gauges were affixed at varying distances on the sample surface to investigate the energy attenuation behavior during its propagation. The results indicated that the reflection coefficient in the impact tests gradually increases with increasing impact velocity, whereas the transmission coefficient exhibits a decreasing trend as impact velocity rises. The failure modes of coal samples are primarily categorized into two types: shear failure and tensile failure. The average particle size of the fragments shows an approximately linear decrease as the impact pressures increase from 0.1 MPa to 0.8 MPa. The energy curves over time for both short and long samples can be categorized into three stages: initial compaction stage, energy storage stage, and energy release stage. The energy dissipation of the long samples decreases exponentially with propagation distance, reaching approximately 50% at a propagation distance of 360 mm.