This research investigates the mechanical properties of Portland Cement Concrete (PCC), incorporating recycled brick powder (RBP) and steel fibers and addressing sustainability and performance enhancement in concrete materials. The experimental design systematically varied RBP content (0-15%) and incorporated crimped steel fibers of two lengths (20 and 50 mm) at dosages of 0%, 0.5%, and 1%. The rationale for these percentages stems from balancing material recycling potential with maintaining concrete structural integrity, with RBP serving as a partial cement replacement to reduce environmental impact and material waste. Comprehensive testing encompassed compressive strength, flexural strength, tensile strength, and impact resistance. Due to the high variability of impact data, a two-parameter Weibull distribution was applied to provide a more robust statistical interpretation of the results. The findings revealed that 5% RBP in plain concrete caused slight reductions in mechanical properties, with decreases of 1.48% in compressive, 1.12% in flexural, 1.42% in tensile, 3.19% in first crack, and 3.03% in failure strengths. Steel fiber incorporation significantly enhanced the concrete's response to static and short-term dynamic loading. The combination of lengths (20-mm and 50-mm steel fibers) demonstrated the greatest impact resistance, offering potential applications in infrastructure elements requiring improved durability, such as bridge decks, industrial flooring, and earthquake-resistant structures. While steel fibers substantially improved fatigue resistance under repeated impact loading, the addition of RBP was observed to partially mitigate these benefits. The impact test data for all mixtures were effectively characterized by the two-parameter Weibull distribution, with R2 values consistently exceeding 0.88, providing a reliable statistical framework for assessing concrete performance. The study contributes to sustainable construction practices by demonstrating a viable approach to incorporating recycled materials without substantially compromising mechanical properties.

OpenPyStruct (Toolkit URL: OpenPyStruct, Repository URL: Data) is an open-source toolkit that provides finite element model based optimization frameworks for generating training data and machine learning models for global structural optimization of indeterminate continuous structures. The key machine learning feature of OpenPyStruct is its ability to optimize single or multiple arbitrary loading and support conditions. The framework utilizes multi-core central processing unit (CPU) and graphics processing unit (GPU)-enhanced implementations integrating OpenSeesPy for structural optimization. PyTorch is used for accelerated computations. Accompanying machine learning scripts enable users to train high-fidelity predictive models such as transformer with diffusion modules, physics-informed neural networks (PINNs), convolutional operations, and contemporary machine learning techniques to analyze and optimize structural designs. By incorporating state-of-the-art optimization tools, robust datasets, and flexible machine learning resources, OpenPyStruct aims to establish a scalable and fully-transparent engine for structural optimization by engaging the structural engineering community in this open-source toolkit.

Ultra-high-performance concrete (UHPC) has been following economic and environmental trends for the past two decades. Limited research has been conducted on the significance of superplasticizers in UHPC products, despite the high costs they entail for projects. The current study assesses UHPC based on rheological properties and mechanical characteristics considering different factors. In this study, the effects of different levels of superplasticizer derived from sulfonated naphthalene formaldehyde (SNF: 0.7%, 0.8%, and 0.9%), silica fume (SF: 15%, 20%, and 25%), and the water-to-binder ratio (w/b: 0.18, 0.20, and 0.22) were examined. Fresh tests such as slump flow, Vicat needle, and squeezing, as well as hardened tests like compressive strength, flexural strength, and electrical resistivity, were conducted. In the analysis, an artificial neural network (ANN) model and a fuzzy logic (FL) model were employed to forecast compressive strength results at 7 and 28 days. The results indicated that a higher SF dosage reduced slump flow and set time, whereas the opposite was observed for SNF and the w/b ratio. Three distinct behaviors were identified in the squeezing flow test findings: (1) specific elastic behavior and low plasticity, (2) extensive plastic behavior and significant dilatancy, and (3) heightened responsiveness to compressive flow rate and material ratio. SNF demonstrated promise in enhancing compressive, flexural, and electrical strength. The prediction models suggested that the FL (error range 3.18–4.36%) and ANN (0.74–1.03%) models performed well in predicting compressive strength at 7 and 28 days. The encouraging findings from this study set the stage for further sustainable and cost-effective construction methods.

High speed video analysis of near-field explosive detonations displays distinct stages of emergent hydrodynamic instabilities in the fireball/shock-air interface. Typically, beyond 10 charge radii, the instabilities experienced large growths giving rise to more chaotic behaviour of the interface and thus an increasing uncertainty in surface velocity. These surface instabilities are suggested as the primary cause of blast parameter variability in the near-field. However, as a deterministic tool, numerical simulation of the detonation process and subsequent blast wave propagation is not able to replicate the stochastic nature of fireball surface instabilities and hence near-field blast parameter variability. Therefore, it is necessary to develop new methods to simulate and characterise the stochastic features of the fireball/shock-air interface. This paper proposes an algorithm to generate an explosive charge element with random shape in finite element model in order to simulate irregularities in the fireball/shock-air interface, and therefore produce variabilities comparable to those from direct observation. The effect of chaotic fireball/shock-air interface on near-field loading is explored through a large number of numerical simulations in order to investigate the statistical distribution of parameters including peak overpressure and impulse. Subsequently, the effect of stochastic detonator location is explored in a similar manner. A computational procedure based on the Monte Carlo Method is proposed to establish a probabilistic model of near-field blast loads, termed PSL-Blast. The reliability of design blast loads calculated using the UFC 3-340-02 design manual is then estimated using PSL-Blast, which suggests that reliability decreases with decreasing scaled distance. Finally, reliability-based safety factors of blast loads are calculated based on different blast settings.

Using construction and demolition (C&D) waste in concrete production is a promising step toward environmental resilience amid the construction industry’s ecological footprint. The extensive history of using bricks in the construction of buildings has resulted in a considerable amount of waste associated with this commonly used material. This study aimed to assess the quality of concrete by examining the effect of replacing cement with varying percentages of recycled brick powder (RBP—0% to 50%). The primary objectives include evaluating the mechanical properties of concrete and establishing the feasibility of using RBP as a partial cement substitute. The investigation of target concrete can be divided into two phases: (i) laboratory investigation, and (ii) numerical investigation. In the laboratory phase, the performance of concrete with RBP was assessed under short-term dynamic and various static loads. The drop-weight test recommended by the ACI 544 committee was used to assess the short-term dynamic behavior (352 concrete discs). Furthermore, the behavior under static load was analyzed through compressive, flexural, and tensile strength tests. During the numerical phase, artificial neural network models (ANN) and fuzzy logic models (FL) were used to predict the results of 28-day compressive strength. The impact life with different failure probabilities was predicted based on the impact resistance results, by combining the Weibull distribution model. Additionally, an impact damage evolution equation was presented for mixtures containing RBP. The results show that the use of RBP up to 15% caused a slight decrease in compressive, flexural, and tensile strength (about 3–5%). Also, by replacing RBP up to 15%, the first crack strength decreased by 7.15% and the failure strength decreased by 6.46%. The average error for predicting 28-day compressive strength by FL and ANN models was recorded as 4.66% and 0.87%, respectively. In addition, the results indicate that the impact data follow the two-parameter Weibull distribution, and the R2 value for different mixtures was higher than 0.9275. The findings suggest that incorporating RBP in concrete can contribute to sustainable construction practices by reducing the reliance on cement and utilizing waste materials. This approach not only addresses environmental concerns but also enhances the quality assessment of concrete, offering potential cost savings and resource efficiency for the construction industry. Real-world applications include using RBP-enhanced concrete in non-structural elements, such as pavements, walkways, and landscaping features, where high strength is not the primary requirement.

Machine learning (ML) methods are becoming more prominent in blast engineering applications, with their adaptability to new scenarios and rapid computation times providing key benefits when compared to empirical methods and physics-based approaches, respectively. However, ML approaches commonly used for blast analyses are regularly provided with inputs relating to domain-specific parameters, restricting their use beyond the initial problem set and reducing their generality. This article presents the ‘Direction-encoded Neural Network’ (DeNN); a novel way to structure an Artificial Neural Network (ANN) to predict blast loading in obstructed environments. Each point of interest (POI) is represented by the proximity to its surroundings and the shortest travel path of the blast wave in order to prime the network to learn the underlying physics of the problem. Furthermore, a bespoke wave reflection equation creates a zone of influence around each point so that obstacles are only captured in the network’s inputs if they would alter the path of the wave. It is shown that the DeNN can predict peak overpressures with mean absolute errors ∼5 kPa for unseen, complex domains of any shape or size, when compared to the results from physics-based numerical models with ∼30 times the solution time of the DeNN. The network is used to develop maps of likely human injury following detonation of a high explosive in an internal environment, with eardrum rupture levels being correctly predicted for over 93% of unseen test points. It is therefore highly suited for use in probabilistic, risk-based analyses which are currently impractical due to excessive computational cost.

Explosions increasingly occur in densely populated, urban locations. Primary blast injuries (PBIs), caused by exposure to blast wave overpressure, can be predicted using injury criteria, although many are based on idealised loading scenarios that do not necessarily reflect real life situations. At present, there is limited understanding of how, and to what extent, blast-structure interaction influences injury risk, and the suitability of injury criteria that assume idealised loading. This work employed computational fluid dynamics to investigate the influence of blast interaction effects such as shielding and channelling on blast load characteristics and predicted PBIs. The validated modelling showed that blast interaction with common urban features like walls and corners resulted in complex waveforms featuring multiple peaks and less clearly defined durations, and that these alter potential injury risk maps. For example, blast shielding due to corners reduced peak overpressures by 43%–60% at locations behind the corner. However, when the urban layout included a corner and a wall structure, higher pressures and impulse due to channelling were observed. The channelling significantly increased the injury risk at the exposed location and reduced the shielding effects behind the corner. In these cases, the application and interpretation of existing injury criteria had several limitations and reduced reliability. This demonstrates that structural-blast interaction from common urban layouts has a significant effect on PBI risk. Specific challenges and further work to develop understanding and reliability of injury prediction for urban blast scenarios are discussed.

A significant amount of scientific effort has been dedicated to measuring and understanding the effects of explosions, leading to the development of semi-empirical methods for rapid prediction of blast load parameters. The most well-known of these, termed the Kingery and Bulmash method, makes use of polylogarithmic curves derived from a compilation of medium to large scale experimental tests performed over many decades. However, there is still no general consensus on the accuracy and validity of this approach, despite some researchers reporting consistently high levels of agreement. Further, it is still not known whether blast loading can be considered deterministic, or whether it is intrinsically variable, the extent of this variability, and the range and scales over which these variations are observed. This article critically reviews historic and contemporary blast experiments, including newly generated arena tests with RDX and PETN-based explosives, with a view to demonstrating the accuracy with which blast load parameters can be predicted using semi-empirical approaches.

As the detonation product cloud from a high explosive detonation expands, an arresting flow is generated at the interface between these products and the surrounding air. Eventually this flow forms an inward-travelling shock wave which coalesces at the origin and reflects outwards as a secondary shock. Whilst this feature is well known and often reported, there remains no established method for predicting the form and magnitude of the secondary shock. This paper details an empirical superposition method for modelling the secondary shock, based on the physical analogy of the secondary loading pulse resembling the blast load from a smaller explosive relative to the original. This so-called dummy charge mass is determined from 58 experimental tests using PE4, PE8, and PE10, utilising Monte Carlo sampling to account for experimental uncertainty, and is found to range between 3.2–4.9

During ultrafast laser ablation at ambient pressure, redeposition of nano-particulates occurs through backwards flux towards the end of the ablation process and is often viewed as undesirable. Here, on the contrary, we report on unique, highly symmetric redeposition patterns observed during ultrafast laser ablation of metals with closely spaced multi-spots in ambient gases. Spot symmetries were altered with a Spatial Light Modulator or beam splitting optics. At low fluence (relative to material ablation threshold), debris is highly confined within the spot patterns, while at higher fluence, jets of debris emanate along axes of symmetry reaching distances far exceeding the spot separations. These phenomena appear universal but depend on the spot proximity, substrate, ambient gas density and pulse energy. The jets, formed at the collision planes between plasma plumes, consist of agglomerated nanoparticle debris, lifted and accelerated by colliding supersonic Mach shocks whose early interactions are imprinted on the debris fields. Numerical simulation using computational fluid dynamics (CFD) of multi-spot ablation in ambient gas supports this view of the phenomena. These observations are relevant to an improved understanding of coalescing shock waves, induced air flows and re-deposition at ambient pressure.

When explosives detonate in a confined space, repeated boundary reflections result in complex shock interactions and the formation of a uniform quasi-static pressure (QSP). For fuel-rich explosives, mixing of partially oxidized detonation products with an oxygen-rich atmosphere results in a further energy release through rapid secondary combustion or 'afterburn'. While empirical formulae and thermochemical modelling approaches have been developed to predict QSP, a lack of high-fidelity experimental data means questions remain around the deterministic quality of confined explosions, and the magnitude and mechanisms of afterburn reactions. This article presents experimental data for RDX- and PETN-based plastic explosives, demonstrating the high repeatability of the QSP generated in a sealed chamber using pressure transducers and high-speed infrared thermometry. Detonations in air, nitrogen and argon atmospheres are used to identify the contribution of afterburn to total QSP, to estimate the duration of afterburn reactions and to speculate on the flame temperature associated with this mechanism. Computational fluid dynamic modelling of the experiments was also able to accurately predict these effects. Understanding and quantifying explosions in complex environments are critical for the design of effective protective structures: the mechanisms described here provide a significant step towards the development of fast-running engineering models for internal blast events.

This study explores the utilization of recycled concrete powder (RCP) as a sustainable alternative to conventional cement in concrete production, focusing on its impact on mechanical properties and impact resistance. Two series of concrete mixtures were prepared: one without and one with 0.5 % polypropylene fibers by RCP-replacing cement at varying proportions (0 %-50 %). Methodologically, the research involved both laboratory tests and predictive modeling using artificial neural networks (ANN) and fuzzy logic (FL). Laboratory evaluations assessed compressive, flexural, and tensile strengths under static loads, as well as impact resistance through repeated drop weight impact (RDWI) tests. Key findings reveal that RCP-incorporation of 15 % leads to negligible reductions in compressive, flexural, and tensile strengths, while higher replacements significantly decrease performance. The addition of polypropylene fibers notably improved mechanical properties across all RCP replacement levels, enhancing compressive strength by up to 23.65 % and flexural strength by 27.49 % at the upper limits of RCP substitution. Accurate predictions of 28-day compressive strength were achieved via the ANN model with an average prediction error less than 1 %. The study also validates that the Weibull distribution effectively describes the impact resistance of concrete mixtures, confirming the advantages of combining RCP with fiber reinforcement to achieve sustainable and high-performance concrete solutions.

Buried charges pose a serious threat to both civilians and military personnel. It is well established that soil properties have a large influence on the magnitude and variability of loading from explosive blasts in buried conditions. In this study, work has been undertaken to improve techniques for processing pressure data from discrete measurement apparatus; this is performed through the testing of truncation methodologies and the area integration of impulses, accounting for the particle size distribution (PSD) of the soils used in testing. Two experimental techniques have been investigated to allow for a comparison between a global impulse capture method and an area-integration procedure from a Hopkinson Pressure Bar array. This paper explores an area-limiting approach, based on particle size distribution, as a possible approach to derive a better representation of the loading on the plate, thus demonstrating that the spatial distribution of loading over a target can be related to the PSD of the confining material.

Urban blasts have become a significant concern in recent years. Whilst free-field blasts are well understood, the introduction of an urban setting (or any complex geometry) gives rise to multiple blast wave interactions and unique flow complexities, significantly increasing the difficulty of loading predictions. This review identifies commonly agreed-upon concepts or behaviours that are utilised to describe urban shock wave propagation, such as channelling and shielding, in conjunction with exploring urban characterisation metrics that aim to predict the effects on global blast loading for an urban blast. Likewise, discrepancies and contradictions are highlighted to promote key areas that require further work and clarification. Multiple numerical modelling programmes are acknowledged to showcase their ability to act as a means of validation and a preliminary testing tool. The findings contained within this review aim to inform future research decisions and topics better.

Avoidance of disproportionate and progressive collapse, often termed ‘fail-safe design’, is a key consideration in the design of buildings and infrastructure. This paper addresses the problem of fail-safe truss topology optimization in the setting of plastic design, where damage is defined as a moveable circular region in which members are considered to have zero strength for that particular load case. A rigorous and computationally efficient iterative solution strategy is employed in both the dual (member adding) and primal (damage-case adding) problems simultaneously, which allows cases of high complexity and many damage cases (maximum of 16290 potential members and 16291 damage cases) to be solved to the global optimum. Common member-based damage definitions (e.g. damage to any one member) are shown to be highly dependent on the nodal grid; in the limiting case completely negating the effect of the fail-safe constraints. The method proposed in this article does not have such limitations, enabling a more sophisticated and robust treatment of fail-safe design. Moreover, the global minimization and high resolutions create new benchmarks for the least-material designs of ‘fail-safe’ structures using rigid-plastic materials. A number of example structures are considered (short cantilever, square cantilever, multi-span truss), and the effects of damage radius, location, and structure rationalisation are discussed.

Historically, most testing with shallow buried charges has focussed on soils which are predominantly quartz (silica)-based. Particle size, moisture content and density have previously been investigated to ascertain their importance, along with other geotechnical parameters, in governing the magnitude of an impulsive output. This has shown that, in order of importance, moisture content, density and particle size drive the total impulse imparted. The work in this paper presents the results of blast testing carried out with carbonate sands to investigate the difference that particle mineralogy (and hence, propensity for breakage) has on both the localised loading and the total impulse using an array of 17 Hopkinson pressure bars known as the Characterisation of Blast Loading (CoBL) apparatus. Carbonate sands are thought to have more friable particles due to their plate-like morphology, as opposed to the rounded morphology of quartz-based sands. Testing was conducted with low moisture content samples and compared with the well-established Leighton Buzzard uniform sand to isolate the effect of particle mineralogy/morphology on the loadings measured. The results show that, despite attaining a 23% lower bulk density, carbonate soils deliver almost identical total impulses (0.7–3.0% higher) when compared with quartz soils for nominally identical moisture contents.

The design of blast-resistant structures and protective systems requires a firm understanding of the loadings imparted to structures by blast waves. While empirical methods can reliably predict these loadings in the far field, there is currently a lack of understanding on the pressures experienced in the very near field, where physics-based numerical modelling and semi-empirical fast-running engineering model predictions can vary by an order of magnitude. In this paper, we present the design of an experimental facility capable of providing definitive spatially and temporally resolved reflected pressure data in the extreme near field (Z<0.5 m/kg1/3). The Mechanisms and Characterisation of Explosions (MaCE) facility is a specific near-field evolution of the existing Characterisation of Blast Loading (CoBL) facility, which uses an array of Hopkinson pressure bars embedded in a stiff target plate. Maraging steel pressure bars and specially designed strain gauges are used to increase the measurement capacity from 600 MPa to 1800 MPa, and 33 pressure bars in a radial grid are used to improve the spatial resolution from 25 mm to 12.5 mm over the 100 mm radius measurement area. In addition, the pressure bar diameter is reduced from 10 mm to 4 mm, which greatly reduces stress wave dispersion, increasing the effective bandwidth. This enables the observation of high-frequency features in the pressure measurements, which is vital for validating the near-field transient effects predicted by numerical modelling and developing effective blast mitigation methods.

Transfer learning offers the potential to increase the utility of obtained data and improve predictive model performance in a new domain, particularly useful in an environment where data is expensive to obtain such as in a blast engineering context. A successful application in this respect will improve existing surrogate modelling approaches to allow for holistic and efficient strategies to protect people and structures subjected to the effects of an explosion. This paper presents a novel application of transfer learning for the prediction of peak specific impulse where we demonstrate that previous knowledge learned when modelling spherical charges can be transferred to provide a performance benefit when modelling cylindrical charges. To evaluate the influence of transfer learning, two artificial neural network architectures were stress tested for three levels of random data removal: the first model (NN) did not implement transfer learning whilst the second model (TNN) did by including a bolt-on network to a previously published NN model trained on the spherical dataset. It is shown the TNN consistently outperforms the NN, with this out-performance increasing as the proportion of data removed increases and showing statistically significant results for the low and high threshold with less variability in all cases. This paper indicates transfer learning applications can be used successfully with considerable benefit with respect to surrogate modelling in a blast engineering context.

Evaluating the blast loads that structures can be subjected to mainly relies on traditional design manuals that are mostly restricted to free-field explosions or to confined settings of simple geometry. However, the blast loads that apply to structures located in denser environments are significantly affected by the complex interactions of the blast wave with the urban topography. Many existing works address the effects resulting from such interactions; however, their scopes are mostly limited to considering a few buildings or streets, while the influence of building porosity on the shielding effect has received limited attention. This work utilizes validated high-fidelity numerical simulations to carry out a detailed investigation of the influence of a building's porosity (categorized as "zero", "low", "medium" and "high") on its capacity to shield virtual facades located at several distances behind the shielding structure. The results show that the shielding effect is mainly bounded below by that behind the high-porosity building and above by that behind the zero or low-porosity building, depending on the standoff distance to the charge. Finally, suitable scaling and modeling approaches including regression and machine learning techniques are applied to devise simplified, yet more general data-driven surrogate tools for computationally inexpensive predictions. These can contribute in helping design engineers evaluate blast loads behind porous structures following explosions in cityscapes.

Recent terror events such as the Manchester Arena bombing and Brussels and Istanbul airport attacks featured improvised explosive devices detonated in crowded internal spaces. A blast wave that propagates in the presence of obstacles will have fundamentally different properties to those of an unimpeded blast wave. Physical processes such as reflection, diffraction, and superposition of multiple wave fronts result in highly complex and situational-dependent loading characteristics which cannot be predicted using simple tools such as those for predicting free-field blast parameters. The influence of blast–obstacle interaction within an internal environment has not yet been studied. This article uses computational fluid dynamics within a probabilistic framework to quantify the influence of obstacle density and positioning on blast loading characteristics. Two mechanisms which alter the properties of a blast wave are studied: ‘channelling’ and ‘shielding’. It is shown that channelling effects are highly localised and result in increased loading near the explosive, the effect of which increases with obstacle density. Shielding is shown to be a cumulative effect which increases with distance from the explosive, and with increasing obstacle density. Whole-domain cumulative density functions are used to derive quantitative descriptors of the loading characteristics and how they vary relative to simple benchmark cases, with a view to providing clear guidance on the development of future predictive tools.