A wide variety of natural catastrophes are induced by coal mining, with fire hazard being one of the most significant threats to underground engineering structures. In recent years, there has been an alarming rise in mine fire accidents due to the abundance of coal deposits around the world. Underground fires and explosions have continuously been the primary reason for a significant proportion of deaths and the destruction of infrastructure over the last few decades. Underground mining fires deplete natural coal resources, have an adverse impact on the environment by releasing hazardous chemicals and greenhouse gases into the atmosphere, and cause subsidence due to coal depletion during the combustion process. This study aims to predict fire danger rating of underground mining production processes by using the application of state-of-the-art unsupervised and supervised machine learning techniques. The developed k-nearest-neighbors-based isometric feature mapping and fuzzy c-means clustering algorithm has shown its dependability and superiority with a higher accuracy and has been advantageous to the monitoring and prevention of fire danger in underground mining production processes. The proposed multi-criteria decision intelligence framework permits early fire detection, providing the emergency response team extra time to respond the critical situations in order to prevent the fire from spreading, hence promoting sustainable, green, climate-smart, environmentally friendly and safe mining engineering operations.

The block caving mining method has become increasingly popular in the last two decades. Meanwhile, Indonesia has several potential ore bodies which have not yet determined suitable mining methods. The references to block caving mining projects worldwide and the potency of metal deposits in Indonesia were reviewed to determine the requirements of ore bodies suitable for mining using the transformed block caving method. This method can be applied on a blocky ore body with a thickness of 200–800 m, various rock mass strengths until 300 MPa, from low to high (from 0.3% Cu until more than 1.0% Cu), but of uniform grade and at a depth from 500 to 2200 m. The technical specifications for running block caving mines have been synthesized, including preparation methods, undercutting strategy, mine design, mining equipment and monitoring. Considering the requirements and the successful practice of the block caving project in the Grasberg Caving Complex as a role model, the Indonesian government should concentrate on the detailed exploration of porphyry deposits and feasibility studies on applying the method to the prospective ore bodies, i.e., Onto, Tambulilato, Tumpangpitu and Randu Kuning. In addition, the exploration method, cost, operation, environment, mining policy and social geology are important aspects worth noting.

In the case of mining in an inclined intrusion using the block caving method, the highest stress is usually concentrated in the seismogenic and abutment zones, especially in the front of the sloping area. In an inclined intrusion of more than 40°, the seismometer network is usually distributed in the facility area where the footwall area is also located. This causes a limitation in microseismic monitoring due to ray coverage. In this study, we conduct a seismometer deployment outside a mining facilities area with borehole seismometers. The study aims to maximize the resolution and minimize the monitoring uncertainty of underground mines. We created two scenarios of seismometer deployment: (i) seismometers are deployed following the intrusion mining level in the mining facility area; and (ii) additional seismometers are deployed in off-facilities areas. Both areas were tested for their raypath responses and sensitivity using the Checkerboard Resolution Test (CRT). The monitoring resolution influenced by the additional borehole seismometers in the off-facilities area can be quantified. The results suggest that the additional seismometers in the off-facilities areas can increase resolution by 30% in the seismogenic and abutment zones.

Caving activity results in an increased induced seismicity which should be monitored to avoid massive and uncontrolled rock damage. This research was conducted at the Deep Mill Level Zone (DMLZ) underground mine, the deepest underground mine in Indonesia operated by PT Freeport Indonesia. This research aims to monitor cave propagation by using 4D tomography with a catalogue of microseismic for 57 days with a total of 14,821 events recorded by 84 stations consisting of 176,265 P phases and 133,472 S phases. The data is divided into four subsets to see the velocity evolution related to cave progress. Checkerboard Resolution Test (CRT) and Derivative Weight Sum (DWS) are used to assess the resolution of the inversion. 3D initial velocity model is constructed based on geological information and coring data. We have succeeded in identifying the interpreted cave propagation of a 60 m extension to the NW at around 100 m above undercut level based on 4D changes in velocity tomogram validate by Time Domain Reflectometry data. The decrease of Vp and Vs in subset 3 is interpreted due to the fracturing processes as the cave progresses. Furthermore, we observe a stress redistribution along with the progress of the cave, which is characterized by high velocities (Vp and Vs) due to compensation for changes in low velocity values ​​in the area in front of the cave, which is starting to collapse. We suggest that a considerable change in the velocity tomogram as an indicator of impending caving.

In construction technology, there are significant efforts to reduce environmental emissions, particularly NH₃ and other pollutants. This study marks the first application of CaCO₃ biomineralization biotechnology in microbially induced calcium carbonate precipitation (MICCP) to enhance mortar properties using the non-pathogenic Neisseria perflava strain SKC/VA-3, which employs carbonic anhydrase mechanisms. The results demonstrated that N. perflava could significantly improve the physical and mechanical characteristics of mortar. Incorporating N. perflava and calcium lactate pentahydrate resulted in a 20% increase in compressive strength and a 14% rise in indirect tensile strength of the mortar. Examination through scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDS) revealed calcite formation within the microstructure of the bio-mortar. Additionally, self-healing assessments indicated that calcite precipitation, driven by bacterial metabolism, also occurred on the cracked surfaces of the bacterial mortar, suggesting potential for reduced maintenance and increased material longevity. This study provides the first report on the use of N. perflava for bio-mortar enhancement and represents a novel biotechnological approach to improving the properties of mortar and other cementitious materials. The utilization of N. perflava in bio-mortar represents a groundbreaking biotechnological advance, potentially enhancing mortar and other cement-based materials. This development contributes to sustainable, durable, and environmentally friendly construction technologies.