Advancing Protective Design Against Munitions



Written by: Ankit Agrawal and Makanzie Kubale |

This year, Hinman participated in the 18th Interaction of the Effects of Munitions with Structures (ISIEMS 2019), a biennial international symposium jointly sponsored by the United States and Germany. This historic symposium is known for bringing together a unique mix of international academics, industry experts, and government institutions having a common goal of advancing state-of-the-art protective design against munitions.

Besides high-quality presentations spaced over an entire week, this year’s two featured speakers – Dr. Peter Warren Singer, (strategist, author, and Senior Fellow at New America) and Mr. Chris Smith (Chief Meteorologist, WJHG-TV) provided a broader context for emerging challenges in protective design. Dr. Singer’s unique insight into how social media has transformed war and politics, as well as his thoughts on weaponizing data, sparked engaging conversations on a fundamental question: what constitutes a real threat. On the other hand, Mr. Smith’s direct experience of living through Hurricane Michael highlighted the need to reliably predict consequences of an uncertain event in an increasingly technology-dependent society.

Hinman’s presentation on designing for the combined effects of explosive loading, flying debris/fragments, and fire showcased a novel way to develop design guidance for a highly complex and uncertain combination of hazard events in an integrated and reliable manner. Besides sharing our own ideas, the Symposium provided us an opportunity to learn and interact with our peers in the field of protective design, leading to three key takeaways for the future of the field:


The notion that statistics for past occurrences define statistics for the future is no longer a reliable measure of future risk. For instance, this is particularly true for non-military infrastructure assets whose engineering and construction were traditionally not concerned with threats posed by munitions. The recent wave of terrorist attacks targeting non-military infrastructure has significantly changed that perception with protective design becoming a more mainstream consideration. For example, a data center for a private business may require a level of passive protection that was once not considered; including the implementation of blast resistant windows, fire protection, curtain walls, and reinforced bollards.

As a result, there is a rapidly evolving need to adapt solutions developed specifically for military applications into protective design for non-military infrastructure, subject to vastly different aesthetic and cost constraints. An objective means to quantify trade-off between aesthetics, cost, and efficacy is crucial to reliably adapt protective design solutions for non-military infrastructure.


Pioneers of Machine Learning (ML) have likened it to fire; the ability to create and control fire transformed humanity, and the same can be said for ML. While ML has already led to major breakthroughs in a variety of fields ranging from autonomous vehicles to natural language processing, it is only beginning to emerge as a possible solution for expediting iterative tasks associated with protective design.

Generating enough data for developing and testing such ML tools is extremely challenging; applying high-fidelity simulations to generate ‘synthetic’ data appears to be a lucrative alternative. However, it is crucial to understand that the limits of applicability of such physics-based simulations might not be reflected in the corresponding ML tools. Consequently, there is a need to develop a better understanding of how to adapt groundbreaking ML technologies for protective design applications.


Protective design for explosive events must be robust against uncertainty arising from both known unknowns – such as event location, magnitude, and quality of the final construction, as well as unknown unknowns. In addition to developing a thorough understanding of the physics of interactions between munitions and structures, quantifying uncertainty will be crucial in establishing the reliability of design solutions for such extreme and uncertain events.

Unlike our understanding of physics around related topics, uncertainty quantification (especially in the context of built infrastructure) has not evolved into deployable solutions in industry. Given the recent advances in our thinking around uncertainty, and the advent of the quantum computer, maybe its high time that uncertainty quantification becomes an essential part of mainstream protective design.

As a world leader in integrated protective design, Hinman is committed to addressing these emerging challenges and providing adaptable solutions for complex infrastructure projects in an ever-changing landscape. We have been trusted by federal, state, and regional agencies as well as private corporations for projects around the world. Our protective design strategies mitigate risk from potentially catastrophic events. Whether the “project” pertains to a single facility, a complex operation, or a portfolio of properties, we help clients understand the level of investment required to adequately protect against known and unknown risk.


Hinman Consulting Engineers is a world leader in protective design. We work with commercial and governmental organizations to design and engineer physical structures and mitigate risk.