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Struck-by accidents (i.e. being hit by a falling object) are a leading cause of traumatic brain injuries in the construction industry. Despite the critical role of hard hats in minimizing such injuries, their overall design has not appreciably changed in decades. Therefore, this study aims to explore the potential benefits of modifying commercially available hard hat designs by incorporating a compliant cantilever and a sacrificial, energy-absorbing structure to enhance their protective capabilities against impacts.

This study involved conducting experimental impact tests to obtain the head acceleration attenuation using hard hats with a variety of compliant cantilever lattice insert designs. These lattice inserts were additively manufactured using three polymeric materials, including polylactide (PLA), acrylonitrile butadiene styrene, high-impact polystyrene and three porosity levels. A Hybrid III head/neck assembly was fitted with each hard hat design, and experimental drop tests were conducted using a 1.8-kg steel impactor dropped from 1.83 m. The maximum acceleration and head injury criterion (HIC) values were obtained for each test.

Analysis of variance revealed that HIC was significantly reduced for all lattices with 56% porosity (p < 0.023) compared to the control (unmodified) hard hat. The most effective insert was found to be a PLA insert with 56% porosity, which reduced the HIC value by 38% compared to the control (unmodified) hard hat, with a statistically significant p-value of 0.018.

The data present in this study reveals that simple and inexpensive modifications can be made to existing hard hat designs to reduce injury risk from overhead impacts.

Heterogeneous and micro-structured materials have been the object of multiscale and homogenization techniques aimed at recognizing the elastic properties of the equivalent continuum. The proposed investigation deals with the mechanical characterization of the heterogeneous material structured metamaterials through analyzing the ultimate strength using the limit analysis of the Representative Volume Element (RVE). To get the desired material strength, a novel finite element formulation based on the derivation of self-equilibrated solutions through the finite elements devoted to calculating the lower bound theorem has been implemented together with the limit analysis in Melàn’s formulation.

The finite element formulation is based on discrete mapping of Volterra dislocations in the structure using isoparametric representation. Using standard finite element techniques, the linear operator V, which relates the self-equilibrated internal solicitation to displacement-like nodal parameters, has been built through finite element discretization of displacement and strain.

The proposed work presented an elastic homogenization of the mechanical properties of an elementary cell with a geometry known in the literature, the isotropic truss. The matrix of elastic constants was calculated by subjecting the RVE to numerical load tests, simulated with a commercial FEM calculation code. This step showed the dependence of the isotropy properties, verified with Zener theory, on the density of the RVE. The isotropy condition of the material is only achieved for certain section ratios between body-centered cubic (BCC) and face-centered cubic (FCC), neglecting flexural effects at the nodes. The density that satisfies Zener’s conditions represents the isotropic geomatics of the isotropic truss.

For the isotropic case, the VFEM procedure was used to evaluate the isotropy of the limit domain and was compared with the Mises–Schleicher limit domain. The evaluation of residual ductility and dissipation energy allowed a measurement parameter for the limit anisotropy to be defined. The novelty of the proposal consisted in the formulation of both the linearized and the nonlinear limit locus of the material; hence, it furnished the starting point for further limit analysis of the structures whose elementary volume has been described through the proposed approach.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

The purpose of this study is therefore to detail an additive manufacturing process for printing TiD parts for implant applications. Titanium–diamond (TiD) is a new composite that provides biocompatible three-dimensional multimaterial structures. Thus, the authors report a powder-deposition and print optimization strategy to overcome the dual-functionality gap by printing bulk TiD parts. However, despite favorable customization outcomes, relatively few additive manufacturing (AM) feedstock powders offer the biocompatibility required for medical implant and device technologies.

AM offers a platform to fabricate customized patient-specific parts. Developing feedstock that can be 3D printed into specific 3D structures while providing a favorable interface with the human tissue remains a challenge. Using laser metal deposition, feedstock powder comprising diamond and titanium was co-printed into TiD parts for mechanical testing to determine optimal manufacturing parameters.

TiD parts were fabricated comprising 30% and 50% diamond. The composite powder had a Hausner ratio of 1.13 and 1.21 for 30% and 50% TiD, respectively. The flow analysis (Carney flow) for TiD 30% and 50% was 7.53 and 5.15 g/s. The authors report that the printing-specific conditions significantly affect the integrity of the printed part and thus provide the optimal manufacturing parameters for structural integrity as determined by micro-computed tomography, nanoindentation and biocompatibility of TiD parts. The hardness, ultimate tensile strength and yield strength for TiD are 4–6 GPa (depending on build position), 426 MPa and 375 MPa, respectively. Furthermore, the authors show that increasing diamond composition to 30% results in higher osteoblast viability and lower bacteria count than titanium.

In this study, the authors provide a clear strategy to manufacture TiD parts with high integrity, performance and biocompatibility, expanding the material feedstock library and paving the way to customized diamond implants. Diamond is showing strong potential as a biomedical material; however, upscale is limited by conventional techniques. By optimizing AM as the avenue to make complex shapes, the authors open up the possibility of patient-specific diamond implant solutions.

The purpose of this study is to investigate and evaluate the impact of varying proportions of reinforcement on the mechanical properties of a modified Al₂O₃-LM6 cast composite under self-pouring temperature conditions. This study aims to determine the optimal mixture proportion of fine powders of Al, Si and xAl₂O₃ (with x values of 2%, 3% and 4%) through the application of design of experiment (DoE) and statistical analysis using the Minitab software. This study also involved evaluating the microstructural estimation and other physical properties of the cast composite to understand the combined effect of the reinforcement proportion on the material’s properties.

The researchers initially mixed the powders through ball milling and then compacted the moisture-free powder mix in a closed steel die. The resulting preforms were heated at the self-pouring temperature in an inert environment to fabricate the final cast composite. By applying DoE and performing an analysis of variance (ANOVA), the researchers sought to optimize the mixture proportion that would yield the best mechanical properties.

The experimental results indicated that a mixture combination of 83.5% Al blended with 12.5% Si and 4% Al₂O₃ led to the greatest improvement in mechanical properties, specifically in terms of increased density, hardness and impact strength. The ANOVA further supported the interaction effect of each processing parameter on the observed results. The results of this study offer valuable insights for the fabrication of modified Al₂O₃-LM6 cast composites under self-pouring temperature conditions. The identified optimal mixture proportion provides guidance for manufacturing processes and material selection to achieve improved mechanical properties in similar applications.

This study focuses on a specific composite material consisting of modified Al₂O₃ and LM6. Although Al₂O₃ and LM6 have been studied individually in various contexts, the combination of these materials and their impact on mechanical properties under self-pouring temperature conditions is a novel aspect of this research. The researchers use DoE methodology, along with statistical analysis using Minitab software, to optimize the mixture proportion and analyze the data. This systematic approach allows for a comprehensive exploration of the parameter space and the identification of significant factors that influence the mechanical properties of the composite.

By using higher moments, this paper extends the quadratic local risk-minimizing approach in a general discrete incomplete financial market. The local optimization subproblems are convex or nonconvex, depending on the moment variants used in the modeling. Inspired by Lai et al. (2006), the authors propose a new multiobjective approach for the combination of moments that is transformed into a multigoal programming problem.

The authors evaluate financial derivatives with American features using local risk-minimizing strategies. The financial structure is in line with Schweizer (1988): the market is discrete, self-financing is not guaranteed, but deviations are controlled and reduced by minimizing the second moment. As for the quadratic approach, the algorithm proceeds backwardly.

In the context of evaluating American option, a transposition of this multigoal programming leads not only to nonconvex optimization subproblems but also to the undesirable fact that local zero deviations from self-financing are penalized. The analysis shows that issuers should consider some higher moments when evaluating contingent claims because they help reshape the distribution of global cumulative deviations from self-financing.

A detailed numerical analysis that compares all the moments or some combinations of them is performed.

The quadratic approach is extended by exploring other higher moments, positive combinations of moments and variants to enforce asymmetry. This study also investigates the impact of two types of exercise decisions and multiple assets.

This study aims to understand how the facesheet size, orientation and core size influence the analytical failure mechanism mode of glass fibre reinforced polymer (GFRP)/polyvinyl chloride (PVC) sandwich structures subjected to three-point bending. The purpose of this study was to develop failure-mode map of GFRP/PVC sandwich structures. Sandwich structures with different facesheet and core thicknesses were used to develop the failure map.

The sandwich structure and facesheet were fabricated using a vacuum-assisted resin infusion method with core sizes of 10, 15 and 20 mm and facesheet thicknesses of 1.5 and 3 mm and were arranged in three different orientations: angle-ply, cross-ply and quasi-isotropic. The key failure modes that occur in sandwich structures were used to predict possible failures in the developed material. Analytical equations were used in MATLAB for each observed failure mode. The probable failure modes, namely, face yielding, core shear and indentation equations, were used to construct the failure maps and were compared with the experimental data.

The boundary of the two failure modes shifts with changes in the facesheet and core thicknesses. The theoretical stiffness of sandwich panels was higher than the experimental stiffness. Based on strength-to-weight ratio, specimens E10-4, A15-8 and E20-8 exhibited the best optimum values owing to their shorter distance to the boundary lines.

In this study, a failure map was used to predict the possible failure modes for different GFRP facesheet orientations and thicknesses and PVC core thickness sandwich structures. Little is known about the prediction of the failure modes of unidirectional GFRP arranged in different orientations and thicknesses and PVC core thicknesses for sandwich structures. Few studies have used failure mode maps with unidirectional GFRP oriented in angle-ply, cross-ply and quasi-isotropic directions as a facesheet for sandwich structures compared to bidirectional mats. This study can serve as a guide for the correct selection of materials during the design process of sandwich structures.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

This study aims to develop a methodology that extracts an architectural concept from a biological analogy that integrates forms and kinetic behavior to identify whether complex forms work better or simple forms with proper kinetic behavior for improving visual comfort and daylight performance.

The research employs a transdisciplinary approach using several methods consisting of a biomimetic functional-morphological approach, kinetic design strategy, case study comparison using algorithmic workflow and parametric simulation and inverse design, to develop an interactive kinetic façade with optimized daylight performance.

A key development is the introduction of a periodic interactive region (PIR), which draws inspiration from the butterfly wings' nanostructure. These findings challenge conventional perspectives on façade complexity, highlighting the efficacy of simpler shapes paired with appropriate kinetic behavior for improving visual comfort. The results show the façade with a simpler “Bookshelf” shape integrated with a tapered shape of the periodic interactive region, outperforms its more complex counterpart (Hyperbolic Paraboloid component) in terms of daylight performance and glare control, especially in southern orientations, ensuring occupant visual comfort by keeping cases in the imperceptible range while also delivering sufficient average spatial Daylight Autonomy of 89.07%, Useful Daylight Illuminance of 94.53% and Exceeded Useful Daylight Illuminance of 5.11%.

The investigation of kinetic façade studies reveals that precedent literature mostly focused on engineering and building physics aspects, leaving the architectural aspect underutilized during the development phase. Recent studies applied a biomimetic approach for involving the architectural elements besides the other aspects. While the biomimetic method has proven effective in meeting occupants' visual comfort needs, its emphasis has been primarily on the complex form which is difficult to apply within the kinetic façade development. This study can address two gaps: (1) the lack of an architectural aspect in the kinetic façade design specifically in the development of conceptual form and kinetic behavior dimensions and (2) exchanging the superficial biomimetic considerations with an in-depth investigation.