Search Documents

By Mick Fritz, Gregory Poole

Construction blasting is always challenging when in close proximity to existing structures, but when you are next to a landmark like Sanford Stadium at the University of Georgia it can get intense. Blasting on this project was performed less than 100 feet from students studying in their dorms at Reed Hall. Some shots are only a few feet from the columns on the stadium that hold up the sky boxes where meetings were being held while blasting was being performed. If a shot goes vertical the rocks would go through the floor of the sky boxes.

Jan 1, 2011

By R. Alford

In the modern world of demolition safety is paramount and unnecessary or unquantifiable risks are not accepted (FasterCapital, 2024) and all risks must be kept As Low As Reasonably Practicable (ALARP) (BSI, 2017). This principle has led to a huge reduction in deaths in the last 50 years and the statistics continue to improve although this is starting to plateau (Beal, 2007). Under normal circumstances, this is now simply part of the normal way work is done and systems are in place to ensure both safety and economic profits are assured. What happens when the environment is less controlled and risks are unknown, such as after a natural disaster, war, an accident or a stand-up from a failed demolition attempt? A dangerous structure cannot responsibly be left alone with a fence around it so some action must be taken, but how can this be done safely and responsibly? An incident in Didcot, England in which half of a power station’s boiler house being prepared for an explosive demolition collapsed suddenly, killing four men in 2016 provides a useful case study of how technology, creativity and planning can be used to conduct complex demolition fully remotely to avoid any further risk to life.

Jan 21, 2025

By Brian Hawkins, David Bradley, Rachel Gooding, Lucas Rodriguez, Patrick Wilson, Kevin Wegman, Dhruv Mittal

The Department of Homeland Security (DHS) has undertaken a probabilistic risk assessment (PRA) approach to quantifying the infrequent, yet high-impact explosive terrorist attacks on the United States (US) homeland and aiding in mitigating that risk through efforts ranging from intelligence gathering, defense postures, detection architectures, partnering with industry to prevent acquisition, and informed medical response. Based on an evaluation of existing model options, a path to the realization of an exterior explosive model (EEM) that builds upon existing models while meeting runtime requirements, maintains consistency with existing DHS chemical, biological, radiological, and nuclear (CBRN) consequence models, and provides sound estimates of the human health consequences of explosive terrorism attacks in open-air urban and rural environments.

Jan 1, 2018

By Thomas E. Lobb, Harry C. Verakis

Significant progress has been made in the reduction of serious injuries and fatalities resulting from mine blasting operations. Despite the progress, injuries and fatalities continue to occur. A leading cause of injuries and fatalities from blasting continues to be inadequate blast area security. Even though significant improvements in technology have been made, insuring adequate blast area security remains a challenge and requires constant vigilance. The advances in technology have created safer blasting products and have improved productivity and economics by enabling large, more efficient and effective blasts. However, as blasts grow larger in size, the complexity of adequately securing the blast area increases even more. Periodically the basic fundamentals involved in blasting safety should be reviewed, particularly for securing a blast site regardless of the size of the planned blast. Factors such as flyrock and toxic fumes must be taken into account to insure the safety of persons and property from the results of a blast. This paper examines the factors related to injuries due to inadequate blasting shelters and blast area security, and identifies mitigation techniques. The key concepts are: (a) accurate determination of the bounds of the blast area, (b) clearing employees from the blast area, (c) effective access control, (d) use of adequate blasting shelters, (e) efficient communications, and (f) training. Fundamentals are reviewed with an emphasis on analyzing task elements and identifying root causes for selected blasting accidents. Mitigating techniques are presented along with discussions and examples.

Jan 1, 2005

By Tom Snyder, John Brulia

Part 4 is the final article by the authors on hazardous materials (HM)transportation safety and compliance for U.S. shippers and carriers who offer and transport commercial explosives in commerce on public highways. This article covers hazardous materials (HM) cargo segregation and securement of non-bulk packaged explosives.

Jan 1, 2015

By Dr. Anthony J. Konya

The modern era has seen a boom in virtual training platforms. This study details the effectiveness & applicability of online training in comparison to in-person training for the drill & blast industry.

Feb 1, 2020

By Ann Barron, Mark Abernethy

Regardless of the care exercised by a blaster, seismic and acoustic effects produced by blasting can be disruptive and alarming to many people. With the general trend toward larger blasts, increased population, and urban sprawl, as well as sensational accounts of explosives - related incidents by the news media, fear and concern often leave residents searching for damage to their property. As a result, many blasting companies today find themselves at the center of litigation at an accelerating rate. Obviously, the best strategy for handling vibration and airblast complaints is to operate in a manner that does not create grounds for them in the first place. During the bidding process, ask questions. See the actual site to perf o rm an “exposure analysis.” Collect as many facts as possible before blasting including geological issues, contractual obligations, zoning, historical use of site, disruption of daily life to local residents, demographics of the surrounding area, as well as any unfavorable public opinions or prejudices connected with local construction projects or mining operations. A consultation with local pre-blast firms may assist in tracking down previous problems in the area.

Jan 1, 2004

By Clara Godoy, Paulo Couceiro, Juan Navarro

The continuous automatization and digitalization of the different stages involved in any underground construction bring new possibilities to optimize the operation. One of the most important requirements for the development of these projects consist of a proper rock mass characterization for optimizing the stages of the drilling, blasting and support cycle. In the last few years the road to digitalization has gone through data science and big data, showing the importance of data gathered to describe the geological and geotechnical environment that surrounds the operations. Since rock characteristics have an important influence in the drilling response, the technological advances in the sensorization of drilling machinery such as the Measurement While Drilling system (MWD) allow to gather the necessary amount of information to be applied in data science processes to evaluate changes in the rock mass with higher resolution than conventional methods. In this paper, Machine Learning techniques have been used to combine data monitored from the drill rigwith scanner profiles of the excavated sections to develop an automatic geotechnical rock mass characterization model able to estimate potential zones of over- or under-break of the remaining rock mass in underground blasting. By comparison of scanner profiles of the excavated sections with the blasthole positions, an Excavated Mean Distance (EMD) between the contour blastholes and the excavated profile has been obtained, which may be considered as damage measure. Considering blasting factors as constant, the model combines the rotary, percussive, hydraulic flushing and the rate of advance of the drilling, and the confinement of the rock mass by depth, to detect zones with different rock properties. This provide an early information of the rock mass condition around the blastholes and the prediction of expected overbreak generated after blasting, being possible to design more efficient bastings, in which each borehole is filled with the right and necessary explosive charge. The analysis comprises data from 57 blasts (around 1700 contour blastholes) and more than 4000 excavated sections.

By Clara Godoy, J. A. Sanchidrian

In this paper, Machine Learning techniques have been used to combine data monitored from the drill rig with scanner profiles of the excavated sections to develop an automatic geotechnical rock mass characterization model able to estimate potential zones of over- or under-break of the remaining rock mass in underground blasting. By comparison of scanner profiles of the excavated sections with the blasthole positions, an Excavated Mean Distance (EMD) between the contour blastholes and the excavated profile has been obtained, which may be considered as damage measure.

Feb 1, 2020

By Hapsari Rosa, Ross Cowie

Conventional blasting activities in the mining process use various types of explosives, with the primary raw materials being Ammonium Nitrate and diesel fuel. The quality and specifications of the two raw materials significantly affect the performance of explosives. One program from the Indonesian Ministry of Energy and Mineral Resources requires mixing 30% Bio Diesel 30 with 70% diesel fuel oil in several industrial sectors, including mining, to reduce greenhouse gas emissions and stabilize CPO prices. This program aims very well for environmental sustainability and the palm oil business. However, it turns out that this change impacts the performance and results of the blasting process in the mining industry, which is the appearance of fumes. This paper will discuss the tests carried out in the laboratory on Bio Diesel 30, the explosive products used, and rock samples were taken from a mine in East Java, Indonesia. The aim is to determine what factors can cause fumes utilizing a mixture of Bio Diesel 30 with pure diesel fuel. The parameters tested were visual differences, infrared spectrometry, density and viscosity, surface tension and contact angle for Bio Diesel 30, stability test for explosives, and the SEM-EDS method for rock samples.

Feb 6, 2023

By Javier Illanes, Felipe Moroni

In recent years, implementation of Artificial Intelligence and Machine Learning model technology has gained ground in almost every aspect of human activities. The mining industry has not been foreign to these applications, with advantages including the creation of new optimization methods and the elimination of repetitive tasks. Those applications have been implemented in multiple operative areas, from preventive conveyor belt maintenance to energy optimization for minimizing environmental impact. The blasting process aims to use the energy liberated by the detonation of explosives to break the rock mass according to specifications that facilitate the comminution process. To optimize this process, the energy delivered must correspond to the necessary amount to exceed the tensional and compressional resistance of the rock mass. If the energy applied is insufficient, the rock will not reach the desired fragmentation level and the processing plant may incur additional costs. On the other hand, if the energy level required is exceeded, we incur increased blasting costs that correspond to excess explosive used. Optimization of this aspect of the mining process has major impact in the processing chain reflected in lower costs, efficient resources consumption and environmental impact. The Design for Outcome technology (DfO), developed by Orica, is used to characterize the rock mass in detail to classify, borehole by borehole, the rock mass relative hardness to deliver the adequate energy level to achieve optimal fragmentation. This technology uses information collected by drill sensors, producing data typically referred to as Measure While Drilling (MWD) data to classify the rock mass into domains that can later be used to apply optimal blast designs. In DfO, the data flow is fully automated, starting with the data produced by the drilling system and uploaded to the cloud, which is then domained using Machine Learning (ML) techniques, and then published for consumption using a web interface. Automated explosives loading rules are then generated for each rock mass domain, ensuring that for each borehole, the adequate explosive energy is delivered to achieve the desired fragmentation results with unprecedented accuracy. The application of machine learning techniques allows us to optimize the blasting process results by leveraging a deeper understanding of the rock mass. Applying these optimization techniques, we obtain considerable economic benefits, decrease resources consumption, and achieve a more sustainable mining operation. The application of DfO in a Chilean mining operation allowed the characterization of their rock mass into four domains and established specific explosive loading rules for each one. A 60kUSD reduction in explosives cost was achieved during the test period, while maintaining the desired fragmentation results and expected dig rate. These outcomes were the result of a comparison between the blasting costs used applying the technology mentioned above and a “traditional” classification of the rock mass with the corresponding blasting practices.

Jan 21, 2025

By Robert Hopler

THE KEYSTONE NATIONAL POWDER COMPANY is an Independent organization, incorporated under the laws of the State of Pennsylvania. It has three dynamite mills near Emporium, Pa., with a daily capacity of 165,000 pounds. It maintains branch offices in practically all the largest cities east of the Rocky Mountains, and has distributing magazines located in every section where there is a demand for high explosives, with facilities for wagon deliveries. Our mills are all of modern type and are equipped with the most improved machinery for the manufacture of high explosives. The efficiency of our laboratory in conjunction with the high class of labor employed, has enabled us to put on the market an explosive, the quality of which is the result of extensive research and scientific knowledge. Our modern and complete equipment for harbor and railroad transportation insures our patrons of prompt deliveries, which is an essential feature particularly to contractors and those whose facilities for storage are limited.

Jan 1, 2013

By D VanDoorselaere

"Image analysis techniques are widely used for blast fragmentation measurement, and various commercial packages are available for this purpose. These packages are very versatile and serve as a useful tool for optimization of blasting practice. But many times, the results provided by image analysis techniques do not match with sieved and predicted data. This leads to the suggestion that understanding the limitations of this technology is as important as understanding its usefulness and versatility.A study was conducted in two stages:(a) Comparison between the results of image analysis and screened data using laboratory scale muck piles.(b) Comparison between predicted (Kuz-Ram) and measured (Image analysis) blast fragmentation results for several bench blasts.During laboratory work, muck piles with known size distributions were analyzed with the help of image analysis technique. A disparity was observed between the sieved and measured results because the image analysis techniques cannot accurately determine the % of fi nes in the muck. During fi eld work about fourteen blasts were monitored. It was observed that predicted (Kuz-Ram) results rarely matched with measured (Image analysis) results. The Kuz-Ram curves were modifi ed by changing the rock factor so that the Kuz-Ram and Image analysis curves match in the vicinity of the 50% passing size. The pairs of curves (modifi ed Kuz-Ram and Image analysis) were divided into three categories depending upon their differences in the coarse and fi nes ranges. The blast design parameters affecting the difference between the pair of curves have been outlined. The suggestions have been made to better utilize the image analysis system."

Jan 1, 2007

By Luan Monteiro, Francisco Biulchi

The AngloGold Ashanti - Cuiaba mine sought innovative approaches to minimize lateral development and stope extraction time while maintaining optimal gold recovery and minimizing dilution. The stope 8GAL EXT 30 was selected as a case study. Traditionally, conventional blasting methods in such a scenario would necessitate either a single mass blast or slot development at each end of the stope. These methods typically compromise recovery and dilution or increase development time and risk. Additionally, conventional wired detonators require workers to re-enter the stope between blasts to load blastholes and connect detonators. The wireless initiation system offers a solution by initiating blastholes using magnetic induction signals that can penetrate rock, air, and water. This technology allows a pre-charged stope to be mined without requiring workers to re-enter the area between blasts to charge and connect blastholes. Engineers from Orica and AngloGold Ashanti designed a new extraction sequence utilizing the wireless initiation system. The process began with the opening of one end of the stope using a conventional slot and wired electronic detonators. Subsequently, pre-loaded rings were fired in three blasts to extract the opposite end and the intersection. The 8GAL EXT 30 stope was successfully mined using this technology enabled method. Each blast was meticulously monitored, and the stope void was scanned to assess blast performance. The results demonstrated a 4.6% improvement in recovery and a 16% reduction in dilution compared to the initial plan, and reduced exposure of people to hazardous locations in the stope. These results represent an additional value of USD 200k, gained from implementing wireless initiation to enable this particular blasting schedule.

Jan 21, 2025

By Charles E. Van Barneveld

RBH Note: the following item is a little different than the usual, in that it shows how explosives were used in the past to assist in putting down holes for iron ore prospecting on the early Mesabi Range of Minnesota. The way explosives were used would not even occur to the modern explorationist, given the equipment available today. The reason for the great difficulty in putting down a hole was that the area was covered with debris from the great glaciers of the past (the same ones that carved out the Great lakes). This debris consisted of boulders, clay, sand, and any other imaginable mixture of materials.

Jan 1, 2013

By Hernan Escalante, Jonathan Alvis, Paulo Alcazar

Constancia Mine, operated by Hudbay Perú and located in the district of Velille, Chumbivilcas province, Cusco, is an open-pit operation characterized by low-grade ore. In this context, operational efficiency and process optimization are essential to ensure long-term profitability. This paper presents an innovative strategy based on the processing of large volumes of data (Big Data), primarily from the drilling and blasting stages. By consolidating approximately 9 million records per month into a georeferenced block model, an analytical platform was developed to directly correlate material fragmentation quality with plant mill throughput. The implemented methodology integrates multiple operational and geometallurgical data sources, enabling dynamic visualization of blasting conditions and their impact on material behavior during crushing and grinding. Through this correlation, operational patterns that maximize plant throughput while minimizing energy consumption and processing costs were identified. Beyond the direct benefits in the plant, this approach significantly improved mine planning by enabling more informed decisions regarding drill pattern design, explosive loading, and blast sequencing. Improved coordination between mine and plant departments was also observed, contributing to better alignment of operational goals and an integrated business perspective. The application of Big Data in this context represents a step toward digital transformation in mining, demonstrating that advanced analytics can be effectively implemented in large-scale, complex operational environments. This solution was validated under real operating conditions and shows high scalability potential for other operations facing similar challenges. In conclusion, this study demonstrates that systematic analysis of drilling and blasting data, when integrated with geological and operational models, can lead to substantial improvements in plant performance. This contributes directly to the competitiveness and sustainability of low-grade mining operations.

Jan 26, 2026

By R. Jacob Cefalo, Miguel Valenzuela

The modern drill and blast engineer operates in an environment often rich with data from drilling data and blast designs vs. actuals to fragmentation distributions and vibration records. Despite this abundance, much of the information generated remains underutilized, often collected as a matter of routine rather than for a means to an end. This paper explores how artificial intelligence (AI) can bridge that gap, transforming raw operational information into actionable insights that lead to more consistent and optimized blasting outcomes. Through AI-driven analysis of drilling logs, burden and spacing measurements in design vs. actual, fragmentation distributions, and vibration records, we can identify hidden patterns and correlations that traditional analytics overlook or that we may not be aware of. These insights enable engineers to proactively adjust designs and loading strategies, ultimately improving performance aspects such as improving fragmentation, minimizing ground vibration, and lowering overall operational costs. The paper examines the industry's reliance on lagging indicators, such as post-blast fragmentation surveys or actual powder factors, and suggests the need to shift toward leading indicators like real-time drill data monitoring and predictive fragmentation modeling. Utilizing AI to forecast outcomes before a blast is carried out allows engineers to take corrective actions early in the process, thereby creating a feedback loop that continuously improves performance. This integration of AI into drilling and blasting practices marks a pivotal opportunity for the explosives engineering field to evolve from reactive to predictive methodologies unlocking a new level of operational intelligence and value creation.

Jan 26, 2026

By David La Rosa, Matthew Craft, Lee Julian

Rules of thumb are ubiquitous in blasting. Typically presented as ratios, they assist with the initial assumptions of blast design and provide guidance for best practice. Examples include the blast hole diameter as a function of bench height, and stemming length as a function of hole diameter. The most common rules of thumb were developed in mid-20th century and it is important for a blaster to know if the rules of thumb are still reliable.

Feb 6, 2023

By O Omidi

Selection of delay times to optimize fragmentation has been a controversial topic among researchers and blasting practitioners. This paper reviews previous experimental information and presents the results of new tests conducted in small blocks constructed using a commercial grout. The effect of the delay on the average fragment size, coarse fragmentation and uniformity of fragmentation is discussed. Fragmentation was coarser at extremely short or extremely long delays, while a fairly constant average size was obtained at a variety of intermediate delays. Uniformity, defined as the slope of the fragmentation distribution through the average fragment size, was poor at short and very long delays and relatively constant at the intermediate delays. Some tests attempted to minimize the effect of gas penetration by lining the boreholes with copper tubes. When this was the case average fragmentation changed marginally, while uniformity was significantly affected by delay and was typically smaller than in the unlined tests.

Jan 1, 2014

By John Arrell, James Stuart

We have developed a tester that is uniquely able to measure the no-fire power level of certain types of electro explosive devices (EEDs). In the past, it has been difficult to establish a no-fire power level for nonlinear conductors, such as semiconductor-bridge (SCB) EEDs, and conductive-mix EEDs.

Jan 1, 2011