Engineering Structures Research Articles

Analytic analysis of free vibration problem of the plate with a rectangular cutout using symplectic superposition method combined with domain decomposition technique

Plates with rectangular cutouts is widely seen in the field of engineering structures. Therefore, it is crucial to examine analytical solutions for free vibration (FV) of these structures. Despite the existence of approximate/numerical methods, analytical solutions are lacking in the literature. In this study, we employ the symplectic superposition method to effectively analyze the FV problems encountered in plates with rectangular cutouts while integrating the domain decomposition technique. To address the issue of irregular geometry, the rectangular cutout plate is divided into multiple sub-plates. By dividing the problem into multiple sub-problems and solving them separately using variable separation and symplectic eigen expansion, we obtain analytical solutions. Finally, we combine the sub-problem solutions to resolve the initial issue. This solution method can be considered a logical, analytical, and systematic approach as it starts with the fundamental governing equation and is derived without assuming forms of solutions. The study presents a comprehensive set of numerical results that include mode shapes (MSs) and natural frequencies (NFs). The results are rigorously validated using the finite element method (FEM) and relevant literature. The symplectic superposition method demonstrates excellent convergence and precise accuracy, making it suitable for analytically modeling more complex mechanical problems of plates.

Seismic performance of carbonated recycled aggregate concrete shear walls

The carbonated modification method is recognized as an effective method to enhance recycled aggregates (RA), but there is currently a lack of research on carbonated recycled aggregate concrete (CRAC) used for structural engineering. This paper investigated the impact of CRAC on the seismic performance of shear walls, quasi-static tests of one natural aggregate concrete (NAC) shear wall and four concrete shear walls with different replacement ratios (30 %, 50 %, 70 %, and 100 %) of carbonated recycled aggregate (CRA) were conducted. The ductility, energy dissipation, stiffness, strength degradation, and lateral displacement components of CRAC shear walls were evaluated. The results indicate that the seismic behavior of the shear walls degraded as the replacement rate of CRA increased. Based on the comprehensive evaluation of failure patterns and seismic performance, the replacement rate of CRA is suggested to be taken as 70 % in the concrete shear walls. Finally, the strut-and-tie model was validated as an effective method for predicting the shear capacity of CRAC shear walls, and the accurate softening coefficient constant C of the strut-and-tie model for CRAC and RAC shear walls was proposed.

The Combination of Electronic Structure and Lattice Strain Engineering for Multi‐Powered Hydrazine‐Assisted Seawater Electrolysis System at High Current Densities

AbstractThe utilization of hydrazine in aiding water electrolysis presents a promising avenue for achieving highly efficient hydrogen production through energy conversion. Herein, bifunctional electrocatalyst of urchin‐like iron, nickle‐codoped cobalt phosphide supported on Ni foam (FeNi‐CoP/NF) is reported. Benefitting from the combined electronic structure and lattice strain engineering by Fe and Ni‐ codoping, the hydrazine‐assisted seawater electrolysis assembled with FeNi‐CoP/NF as both electrodes can achieve an industrial‐level current density of 1.5 A cm−2 at a record‐setting voltage of 163 mV at 70 °C and sustain stable operation for hundreds of hours in seawater environment. The existence of a potential coincidence region lends credibility to the feasibility of implementing self‐activated hydrazine‐assisted seawater electrolysis. Based on theoretical calculations, the separate and synergistic effects of electronic structure and lattice strain engineering are investigated and an integrated illustration of these two strategies on enhancing hydrogen evolution and hydrazine oxidation activity is provided. Moreover, an innovative multi‐powered hydrogen generation system featuring a direct hydrazine fuel cell, rechargeable Zn‐hydrazine battery, and hydrazine‐assisted seawater electrolysis is proposed, underscoring the unique advantages of hydrazine oxidation reaction and its prospective contribution to electrochemical energy conversion technologies powered by sustainable sources.

Unlocking the Potential of Organic-Inorganic Hybrid Manganese Halides for Advanced Optoelectronic Applications.

Organic-inorganic hybrid manganese(II) halides (OIMnHs) have garnered tremendous interest across a wide array of research fields owing to their outstanding optical properties, abundant structural diversity, low-cost solution processibility, and low toxicity, which make them extremely suitable for use as a new class of luminescent materials for various optoelectronic applications. Over the past years, a plethora of OIMnHs with different structural dimensionalities and multifunctionalities such as efficient photoluminescence (PL), radioluminescence, circularly polarized luminescence, and mechanoluminescence have been newly created by judicious screening of the organic cations and inorganic Mn(II) polyhedra. Specifically, through precise molecular and structural engineering, a series of OIMnHs with near-unity PL quantum yields, high anti-thermal quenching properties, and excellent stability in harsh conditions have been devised and explored for applications in light-emitting diodes (LEDs), X-ray scintillators, multimodal anti-counterfeiting, and fluorescent sensing. In this review, the latest advancements in the development of OIMnHs as efficient light-emitting materials are summarized, whichcovers from their fundamental physicochemical properties to advanced optoelectronic applications, with an emphasis on the structural and functionality design especially for LEDs and X-ray detection and imaging. Current challenges and future efforts to unlock the potentials of these promising materials are also envisioned.

Structural engineering of metal-organic framework catalysts for efficient conversion of glucose into 5-HydroxyMethylFurfural

The research on sustainable and efficient routes to produce valuable chemicals from renewable biomass has garnered considerable attention in recent years. Among these chemicals, 5-hydroxymethylfurfural (5-HMF) holds significant promise as a versatile platform molecule for various applications in the chemical industry. This study investigates the production of 5-HMF from glucose using zirconium-based metal-organic frameworks (Zr-MOFs) as environmentally friendly catalysts, an alternative to the traditional homogeneous catalysts. UiO-66 and its functionalized derivatives (e.g. UiO-66(NH2),UiO-66(COOH)2, and UiO-66(OH)2) were synthesized to study the effect of the functional groups on the dehydration reaction. The synthesized MOFs were fully characterized using powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), N2 adsorption-desorption, Infrared spectroscopy (IR), Pyridine-Fourier Transform Infrared spectroscopy (Py-FTIR), and scanning electron microscopy (SEM). The catalytic performance of the synthesized MOFswas evaluated for the dehydration of glucose to 5-HMF in DMSO and further optimized in other monophasic and biphasic systems. Results indicated that UiO-66(COOH)2 exhibited exceptional catalytic activity for the conversion of glucose to HMF, with a significantly higher HMF yield of 44 % compared to other reported MOF catalysts and to that of H2SO4, which yielded 35 % at a temperature of 140 °C and a 6 h reaction time. This is mainly attributed to the effect of the bifunctional active acid sites of the metal nodes and the functional group of the organic linker (free carboxylic groups), which acted as the main source of Lewis and Brønsted acid sites respectively, compared to UiO-66. Additionally, the results suggested that the catalyst with higher acid density, higher relative mesopority, and a moderate B/L ratio could enhance glucose dehydration regardless of the surface area. Furthermore, the catalyst demonstrated excellent reusability and stability, with a decrease of only 10 % after four reaction cycles. The recycled catalyst revealed no structural alterations in its framework. This study contributes to the growing body of research on Zr-based MOF as a stable and efficient catalyst for glucose valorization to produce 5-HMF under mild reaction conditions.

Metaheuristic algorithm inspired by enterprise development for global optimization and structural engineering problems with frequency constraints

In this study, a novel metaheuristic optimization algorithm inspired by the enterprise development (ED) process was developed. This process encompasses tasks, structure, technology, and human interactions. A mechanism for switching activities was utilized to determine each step by updating the searched solutions. The ED optimizer underwent evaluation against 50 mathematical functions and 54 IEEE Congress on Evolutionary Computation (CEC) functions, and it was compared with six up-to-date algorithms, three winners of CEC 2020 in real-world single-objective constrained optimization, and ten well-known algorithms. The evaluation results revealed that the ED optimizer outperformed the algorithms in mathematical tests. Using finite element simulation, this novel method was applied to minimize steel structure component weights under various frequency constraints. The engineering designs included a 37-bar planar truss, a 52-bar dome, a 72-bar space truss, a 600-bar dome, and a 1,410-bar dome. Optimization results for these structures showed that the developed ED optimizer yielded the optimal solution and required fewer function evaluations. In conclusion, the ED optimizer is a superior metaheuristic algorithm that can effectively solve complex structural design problems.

Numerical Analysis of the Cylindrical Shell Pipe with Preformed Holes Subjected to a Compressive Load Using Non-Uniform Rational B-Splines and T-Splines for an Isogeometric Analysis Approach

In this paper, we implement the finite detail technique primarily based on T-Splines for approximating solutions to the linear elasticity equations in the connected and bounded Lipschitz domain. Both theoretical and numerical analyses of the Dirichlet and Neumann boundary problems are presented. The Reissner–Mindlin (RM) hypothesis is considered for the investigation of the mechanical performance of a 3D cylindrical shell pipe without and with preformed hole problems under concentrated and compression loading in the linear elastic behavior for trimmed and untrimmed surfaces in structural engineering problems. Bézier extraction from T-Splines is integrated for an isogeometric analysis (IGA) approach. The numerical results obtained, particularly for the displacement and von Mises stress, are compared with and validated against the literature results, particularly with those for Non-Uniform Rational B-Spline (NURBS) IGA and the finite element method (FEM) Abaqus methods. The obtained results show that the computation time of the IGA based on the T-Spline method is shorter than that of the IGA NURBS and FEM Abaqus/CAE (computer-aided engineering) methods. Furthermore, the highlighted results confirm that the IGA approach based on the T-Spline method shows more success than numerical reference methods. We observed that the NURBS IGA method is very limited for studying trimmed surfaces. The T-Spline method shows its power and capability in computing trimmed and untrimmed surfaces.

Stabilizing hexagonally close-packed phase in single-component block copolymers through rational symmetry breaking

Despite being predicted to be a thermodynamically equilibrium structure, the absence of direct experimental evidence of hexagonally close-packed spherical phase in single-component block copolymers raises uncomfortable concerns regarding the existing fundamental phase principles. This work presents a robust approach to regulate the phase behavior of linear block copolymers by deliberately breaking molecular symmetry, and the hexagonally close-packed lattice is captured in a rigorous single-component system. A collection of discrete A1BA2 triblock copolymers is designed and prepared through an iterative growth method. The precise chemical composition and uniform chain length eliminates inherent size distribution and other molecular defects. Simply by tuning the relative chain length of two end A blocks, a rich array of ordered nanostructures, including Frank−Kasper A15 and σ phases, are fabricated without changing the overall chemistry or composition. More interestingly, hexagonally close-packed spherical phase becomes thermodynamically stable and experimentally accessible attributed to the synergistic contribution of the two end blocks. The shorter A blocks are pulled out from the core domain into the matrix to release packing frustration, while the longer ones stabilize the ordered spherical phase against composition fluctuation that tends to disrupt the lattice. This study adds a missing puzzle piece to the block copolymer phase diagram and provides a robust approach for rational structural engineering.

Postbuckling behaviour of anisotropic shear deformable laminated cylindrical shells with general imperfection distribution subjected to torsion

Composite cylindrical shells belong to typical basic structure in engineering, whose buckling and postbuckling behaviours determine the stability and safety of overall structures under complex service loadings. In this paper, a nonlinear mechanical model for anisotropic laminated composite cylindrical shells with general distributed initial imperfection is derived to analyse the buckling and postbuckling behaviours under torsional loads, in which the general distributed imperfections are represented by a generic two-dimensional imperfection function reconstructed by using measured data. Based on a higher order shear deformation shell theory, the governing equations are established with consideration of von Kármán-Donnell-type of kinematic nonlinearity and the coupling effects of extension/twist, extension/flexural and flexural/twist. By using a two-step perturbation technique, a new set of explicit solutions is obtained to determine the buckling loads and postbuckling equilibrium paths of the torsional composite cylindrical shells. Furthermore, the internal quantitative relationship between the shear stress with torsional characteristics along with an associate compressive stress of anisotropic laminated shell is for the first time obtained. Numerical analyses are performed to validate the accuracy and effectiveness of the present explicit solutions, and obtain the postbuckling response of different types of imperfections, anisotropic laminated cylindrical shells with different values of shell parameters.

Microfluidic Synthesis of Multifunctional Micro-/Nanomaterials from Process Intensification: Structural Engineering to High Electrochemical Energy Storage.

Multifunctional micro-/nanomaterials featuring functional superiority and high value-added physicochemical nature have received immense attention in electrochemical energy storage. Microfluidic synthesis has become an emergent technology for massively producing multifunctional micro-/nanomaterials with tunable microstructure and morphology due to its rapid mass/heat transfer and precise fluid controllability. In this review, the latest progresses and achievements in microfluidic-synthesized multifunctional micro-/nanomaterials are summarized via reaction process intensification, multifunctional micro-/nanostructural engineering and electrochemical energy storage applications. The reaction process intensification mechanisms of various micro-/nanomaterials, including quantum dots (QDs), metal materials, conducting polymers, metallic oxides, polyanionic compounds, metal-organic frameworks (MOFs) and two-dimensional (2D) materials, are discussed. Especially, the multifunctional structural engineering principles of as-fabricated micro-/nanomaterials, such as vertically aligned structure, heterostructure, core-shell structure, and tunable microsphere, are introduced. Subsequently, the electrochemical energy storage application of as-prepared multifunctional micro-/nanomaterials is clarified in supercapacitors, lithium-ion batteries, sodium-ion batteries, all-vanadium redox flow batteries, and dielectric capacitors. Finally, the current problems and future forecasts are illustrated.

Optimized quantum LSTM using modified electric Eel foraging optimization for real-world intelligence engineering systems

The integration of metaheuristics with machine learning methodologies presents significant advantages, particularly in optimization and computational intelligence. This amalgamation leverages the global search capabilities of metaheuristics alongside the pattern recognition and predictive prowess of machine learning, facilitating enhanced convergence rates and solution quality in complex problem spaces. The Quantum Long Short-Term Memory (QLSTM) emerges as a highly efficient deep learning model tailored to tackle such intricate engineering problems. The QLSTM's architecture, comprising data encoding, variational, and quantum measurement layers, facilitates the effective encoding and processing of civil engineering data, leading to heightened prediction accuracy. However, the task of determining optimal values for QLSTM parameters presents challenges due to its NP-problem nature and time-consuming characteristics. To address this, we propose an alternative technique to optimize the QLSTM based on a modified Electric Eel Foraging Optimization (MEEFO). The MEEFO is a modified version of the original EEFO that applies triangular mutation operators to boost the search capability of the traditional EEFO. Thus, the MEEFO optimizes the QLSTM and boosts its prediction performance. To validate the efficacy of our proposed method, we conduct comprehensive experiments utilizing five real-world engineering datasets related to construction and structure engineering. The evaluation outcomes unequivocally demonstrate that the MMEFO significantly enhances the performance of the QLSTM.

Heterostructure-anchored 3D CNT-bridged graphene architecture via layer-by-layer structural engineering for thick electrodes of supercapacitors

The emerging thick electrode concept is dedicated to maximize active material areal loading at the device-scale through straightforward structural designs to meet the high energy density demand. Nevertheless, overcoming the sluggish charge kinetics and complex manufacturing route in thick composite electrodes is still challenging. Herein, a novel all-laser structural engineering protocol is developed to construct MnO-Mn3O4/MnS heterostructure-anchored 3D porous graphene architecture with reinforced carbon nanotube (CNT) interface (MHGC) by layer-by-layer laser induction of polyethersulfone film containing manganese acetate precursors without introducing any templates or catalysts. Creatively, along with the synchronous generation of porous graphene and multivalent manganese compounds heterostructures, a uniform distribution process of nanoparticles is integrated into a one-step in-situ laser irradiation, which is unique and straightforward for preparing thick composite electrodes. Besides, the interlayer bridging of CNT network between MnO-Mn3O4/MnS/graphene layers accelerates electron transport, accompanied by the ameliorative deep diffusion of ions by open graphene macropores derived from laser-assisted vaporization. Thanks to the configuration of efficient electronic bridge and ion channel, combined with multi-heterointerface and fast surface reaction kinetics endowed by heterostructure nanoparticles, MHGC electrode with a thickness of 442 μm delivers a high specific capacitance of 954.5 mF cm−2 (at 0.5 mA cm−2). The assembled symmetrical supercapacitor with LiCl aqueous electrolyte exhibits a broadened potential window of 1.3 V, obtaining a prominent energy density (39.99 μWh cm−2) and power density (1625 μW cm−2). This advanced laser engineering and enabled thick electrodes open a brand-new avenue in burgeoning energy chemistries, not limited to supercapacitors and rechargeable batteries.

Effect of Unit Cell Rotation on Mechanical Performance of Selective Laser Melted Gyroid Structures for Bone Tissue Engineering

Gyroid lattice structures, inspired by the natural interconnectivity of human bone, show promise in bone tissue engineering (BTE). This study explores the mechanical properties, and failure mechanisms of selective laser melted (SLM) Gyroid Ti6Al4V lattice structures with varying unit cell rotation angles (RA) of 0∘ (G0), 30∘ (G30), and 60∘ (G60) around the x-axis. The study assesses their mechanical and energy absorption properties through quasi-static compression testing and elastoplastic finite element (FE) analysis. Despite similar relative densities for all designed lattices, the RA variation significantly impacts mechanical performance. G30 exhibits superior load-bearing capacity, with higher modulus of elasticity, yield strength, ultimate strength, and plateau stress. It also has the highest energy absorption capacity, while G60 shows the highest surface area (SA) and surface area-to-volume ratio (SA/VR). These findings highlight the potential of Gyroid lattices for bone replacements due to their tunable mechanical and biological responses.

Effects of Deformation-Induced BCC Martensitic Transformation on Uniaxial Ductility and Biaxial Stretchability in Metastable Ferrous Medium-Entropy Alloys

This study investigated the effects of deformation-induced martensitic transformation (DIMT) on the stretchability and fracture behavior of thin sheets made from ferrous medium-entropy alloys (MEAs). Analyses of punch load-stroke curves from Erichsen tests revealed that DIMT occurred in the 60 at% Fe MEA, 64 at% Fe MEA, and 304 stainless steel (60Fe, 64Fe, and 304SS alloys, respectively). This DIMT affects necking onset, uniform elongation, and the Erichsen index (EI) under diverse stress conditions. DIMT plays a significant role in strain hardening and mechanical instability during uniaxial tests, enhancing strain hardening through martensite formation and also initiating necking due to non-uniform deformation. Particularly in 64Fe, DIMT-induced early necking resulted in reduced uniform elongation. On the other hand, biaxial Erichsen test results demonstrated further strain hardening due to martensite-induced strain accommodation, leading to enhanced resistance to deformation during biaxial loading. The distinctive shapes of punch load-stroke curves in biaxial tests, compared to stress-strain curves from uniaxial tensile tests, are a result of the interaction between DIMT and stretching directions, as well as its alloy-dependent phase stability. In 60Fe, transformed martensite contributed to strain hardening and delayed necking initiation, while extensive martensite transformation in 64Fe enhanced strain hardening further. This understanding of the complex correlation between DIMT, strain hardening, and mechanical instability holds potential for alloy optimization and developing processing strategies, applicable to automotive manufacturing and structural engineering industries.

Superamphiphilic aerogels with 2D lamellar structure of gelatin-tuned 3D supramolecular network of collagen fibers for high-performance separation of surfactant-stabilized emulsified oily wastewater

Superwetting aerogel is a promising alternative for the remediation of emulsified oily wastewater for its high porosity combined with extreme wettability enabled high separation performances to emulsion wastewater. However, it remains challenging for superwetting aerogels to accomplish high-performance dual separation to surfactant-stabilized oil-in-water (O/W) and water-in-oil (W/O) emulsions with high stability. Herein, an environmentally benign superamphiphilic composite aerogel was prepared by a green synthesis route that relied on the utilization of natural amphiphilic biomass. Collagen fibers (CFs) were utilized to construct the three-dimensional (3D) supramolecular skeleton of aerogel to provide high storage capacity of water/oil and outstanding capillary effect to boost the mass transfer. The two-dimensional (2D) lamellar structure of gelatin (Gel) was further grown on the skeleton of CFs aerogel to play the role for simultaneously enhanced demulsifying capability and spreading of emulsions. The as-prepared superamphiphilic aerogel enabled the separation of highly stable surfactant-stabilized O/W and W/O emulsions with high separation efficiency and flux. Excellent recycling performances and anti-fouling performance were also confirmed. Our investigations therefore demonstrated that the structural engineering of superamphiphilic aerogel is a promising way to realize high-performance dual separation of surfactant-stabilized O/W and W/O emulsion wastewater.Graphical

Wetting-induced collapse of loess: Tracing microstructural evolution

Loess is a silt-dominated, clastic yellow-to-yellowish brown aeolian sediment with high porosity and low density. The metastable microstructure of loess makes it highly susceptible to collapse, which plays a major role in landform evolution, geohazard development and engineering damages, particularly the widespread occurrences of settlement, deformation and cracking of civil engineering structures. Differing from the existing investigations based on pre- and post-collapse comparison, the present study is to report the microstructural changes during the collapse process and to determine how each microstructural constitutive element participates in this process. A series of laboratory tests was conducted on undisturbed loess specimens to simulate and capture snapshots of microstructure at five representative points during the entire collapse process. Results show that the loess microstructure becomes more homogeneous as the outstanding macropores are crushed into smaller (meso- and mini-pores) pores, the number of pores is dramatically increased, and the pore throats become narrower. The pore shapes have no obvious changes and remain elongated and rough-edged morphology from the point of view of statistic. Regarding solids, the pre-collapse unstable point-to-point contacts tend to transform into relatively stable edge type contacts, whilst coarse particles tend to reorient along the horizonal direction. XRD, SEM and EDS analysis indicates that these changes in the nature and distribution of pores and particles result from (1) the breakage and disintegration of moderately to highly weathered particles, (2) the disintegration of the matrix due to the swelling of clay minerals and the dissolution of calcium carbonates, (3) the consequential modifications of the microscopic forces which lead to slipping, rotation and therefore dislocation of particles, and (4) the collapse of mesoscale force chains along particles, reforming on smaller scales with larger numbers, shorter lengths and horizontal alignments.

The prediction on non-Gaussian characteristics of wind pressure for the long-span roof in the mountainous area using proper orthogonal decomposition–deep learning framework

To make an accurate prediction of the non-Gaussian characteristics of wind pressure for the long-span roof, this study combines the proper orthogonal decomposition (POD) technique, convolutional neural network (CNN), and long short-term memory (LSTM) network to propose a novel POD-CNN-LSTM framework. Then, the proposed framework was well validated based on the wind tunnel testing of a long-span roof structure, and some error criteria, such as mean square root error and correlation coefficient, were adopted to evaluate the prediction accuracy of the non-Gaussian characteristics. Furthermore, two other methods, POD-CNN and POD-LSTM, were also used to conduct a comparative study. The obtained results illustrate that compared to POD-CNN and POD-LSTM, the proposed framework can achieve better performance on the pulsating wind pressure coefficient. For predictions of non-Gaussian characteristics, the output results of the proposed POD-CNN-LSTM show fewer errors, which means the predictions are close to the measured results, including skewness, kurtosis, and wind pressure probability density distributions. To summarize, the proposed POD-CNN-LSTM framework shows superiority over others, which means the proposed framework has good potential for the practical application of non-Gaussian prediction of the engineering structure.

High-dimensional multi-objective optimization of coupled cross-laminated timber walls building using deep learning

This paper introduces a deep learning-based multi-objective optimization framework, applying for advancing the design of the Cross-Laminated Timber Coupled Wall system. While traditional optimization methods often struggle with the curse of dimensionality in high-dimensional problems, the approach proposed in this study employs an autoencoder to effectively reduce the dimensionality of the design space. Subsequently, a neural network establishes a mapping between input variables and latent spaces, with another neural network forming the crucial link between these latent variables and the output responses. The proposed framework is integrated into the design process of a 20-story Cross-Laminated Timber Coupled Wall system, where uncertainties inherent in connection elements are systematically addressed to optimize the structural parameters. The non-dominated sorting genetic algorithm-II is utilized to estimate optimal design variables by minimizing three conflicting objective functions, thereby generating a Pareto front. This optimized design is then bench-marked against three deterministic models with varying coupling beam shear strengths. A two-dimensional numerical model developed in OpenSees facilitates nonlinear time history analysis using 50 bi-directional ground motion records, representative of the seismicity of Vancouver, Canada. The results of this study not only highlight the efficacy of the deep learning-based framework in enhancing the structural integrity and resilience of high-rise timber structures in seismic regions but also significantly contribute to the evolution of computational approaches in structural engineering.

Breach diameter prediction of reinforced concrete wall under contact/near-contact explosives using deep neural network

In recent years, the use of Machine Learning (ML) has been expanded to several engineering fields with applications in structural engineering. Deep Neural Network (DNN) models have been widely implemented to predict the response of structural systems under conventional loading. The accuracy of these DNN models relies on the size of the dataset used, which can vary from hundreds to thousands of data points, typically formed by images, test data, and/or finite element models built for a specific environment. Such dependency becomes a limitation when DNN models are intended to be used in blast analysis and/or design, as data are typically scarce or restricted. This paper introduces the implementation of an ML/DNN model for the prediction of breach diameter of a reinforced concrete target subjected to contact and near-contact explosions. The DNN model predicts the breach diameter using experimentally collected concrete breach data. The DNN model considers several parameters related to the charge (e.g., type, geometry, orientation, casement properties) and wall target (e.g., material properties, geometry, reinforcement, fiber content), as well as proximity between the target and explosive. It was found that charge geometry, charge placement type, target thickness, scaled distance, and fiber content in the mix were important features affecting the breach diameter prediction.

First principles study to investigate structural, optical properties and bandgap engineering of XSnI3(X=Rb, K, Tl, Cs) materials for solar cell applications

First principles study to investigate structural, optical properties and bandgap engineering of XSnI3(X=Rb, K, Tl, Cs) materials for solar cell applications