When combined with high-speed photography technology, the digital image correlation (DIC) method provides an excellent photographic image processing capability that can be used to convert the evolving full-field surface properties of structures to sets of two-dimensional (2D) or three-dimensional (3D) coordinate values. In this study, the applicability of the DIC method and high-speed cameras in underground mining was investigated by measuring the displacement and vibration of rock pillars caused by blasting. This technique is used extensively in engineering and is increasingly being applied to new fields. As a result of comparing the DIC results for blast vibration with the measured values of the contact sensor through field tests, the maximum displacement and vibration speed were found to be 86% and 93% accurate, respectively. In addition, the obtained values appeared similar to those predicted through numerical analysis. Field test results indicate that both methods yielded similar results. Therefore, it is concluded that it is feasible to use the DIC and high-speed camera to measure ground displacements and vibrations from blasting in underground mining. In addition, the system conditions required for blast vibration measurement were considered by comparing the accuracy with the existing measurement methods.

In this study, the pull-out behavior of a tunnel-type anchorage was examined by considering both geometric and rock joint characteristics. Three-dimensional finite element analyses were performed with reference to the tunnel-type anchorage cases designed and constructed in Korea. The factors influencing the anchorage response were analyzed: the enlarged part, anchorage spacing, joint orientation, spacing, and the shear strength of the rock joints. According to the numerical studies, the size of the enlarged part influenced the failure shape of the tunnel-type anchorage. It was found that the anchorage spacing, the relationship between the tunnel-type anchorage, and the joint orientation and spacing greatly influenced the pull-out behavior of the anchorage. Additionally, the friction angle had a larger impact on the anchorage’s pull-out resistance than the cohesion between the rock joints.

In this study, numerical analyses have been performed for a reasonable gravity-type anchorage design. The emphasis is on evaluating the effect of the passive earth pressure for gravity-type anchorages under pullout loading. Three-dimensional FE analyses were performed for different types of bedrock and embedded depths. Based on this study, it is found that the displacement of the gravity-type anchorage decreased with increasing embedded depth due to the increase in the passive resistance in front of the anchorage. It is also found that the resistance due to passive earth pressure in front of the anchorage accounts for approximately 10–30% of the total resistance and thus represents a significant improvement in the prediction of the realistic resistance for gravity-type anchorages subjected to pullout loads.

In this study, a small scale experiment of an earth retaining wall was conducted to develop a real-time and full-field noncontact measurement system for earth retaining walls in urban areas. The collapse behavior of the structure was reproduced using a model of an earth retaining wall, and the deformation and displacement of the entire surface of the small scale structure were measured using a 3D image-based digital-image correlation method employing a stereo camera system. The measured displacement was compared with the actual displacement by using high-precision hexapod equipment in the experiment, and the results confirmed a high level of precision.

Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within the time series data. This research includes preprocessing of measurement data for inclinometer, performance evaluation based on various time series data lengths and input variable conditions, and demonstrates that the LSTM model offers statistically significant improvements in predictive performance over the ARIMA model. In addition, by combining LSTM with attention mechanism, attention-based LSTM (ATLSTM) is proposed to improve the short- and long-term prediction performance and solve the problem of excavation site domain change. This study presents the advantages and disadvantages of major time series analysis models for the stability evaluation of mud walls using geotechnical inclinometer data from excavation sites, and suggests that time series analysis models can be used effectively through comparative experiments.