Design Of Efficient Structures Research Articles

Mechanical characterization of intact rock under polyaxial static-dynamic stress states

Accurate assessment of mechanical behaviour of rock is essential for safe and efficient design of structures on or within rock mass particularly under harsh in-situ stress conditions. Insights from rock engineering practices have revealed that rockbursts can occur at various scales and at any time during mining activities. These violent failure phenomena can be triggered by quasi-static stress redistribution resulting from mining activities as well as dynamically induced loads from seismic events. Moreover, the mechanical behaviour of rock has proven to differ under static and dynamic stress conditions, showing rate-dependent characteristics. Hence, this study aims to thoroughly investigate the triggers that can lead to rockburst events and provide insights into the brittle fracturing process under various stress conditions. To achieve this, a comprehensive set of static and dynamic laboratory tests was conducted on coal samples. The static experiments were conducted under uniaxial and triaxial conditions where the samples were tested at the confining pressures up to 20 MPa. For the dynamic tests, the samples were first pre-stressed uniaxially, biaxially and polyaxially at different ranges of static stresses up to 30 MPa hydrostatically and non-hydrostatically. Then, the high impact velocity up to 21.5 m/s was applied along the maximum stress direction to provide dynamic loading. The influences of loading condition, strain rate and confinement on the mechanical behaviour of coal were investigated. The fracturing process of samples based on their uniaxial, biaxial and polyaxial pre-stressing conditions and various strain rates were analysed to confirm the significant role of loss of confinement and strain rate in severe high-energy fragmentation of coal or so-called “rockburst” under both static and dynamic loadings.

Study of the Photovoltaic Parameters of Inorganic Solar Cells Based on Cu<i>2</i>O and CuO

A theoretical study of the photovoltaic parameters of inorganic solar cells based on ZnO/Cu2O and ZnO/CuO heterojunctions was carried out to improve the energy conversion efficiency. The influence of the thickness, charge carrier concentration and band gap of Cu2O and CuO films, as well as ZnO, on the photovoltaic parameters of solar cells has been studied. The simulation results showed that the efficiency of solar cells is significantly affected by the contact potential difference, the diffusion length of minority charge carriers, the amount of generated photocurrent and the recombination rate. The maximum efficiency of a solar cell based on ZnO/Cu2O was obtained equal to 10,63%, which is achieved with a band gap, thickness and charge carrier concentration in Cu2O equal to 1.9 eV, 5 μm and 1015 cm–3 and band gap, thickness and the concentration of charge carriers in ZnO is equal to 3,4 eV, 20 nm and 1019 cm–3, as well as the displacement of the edges of the conduction bands is 0.8 eV. For a solar cell based on ZnO/CuO, a maximum efficiency of 18.27% was obtained with a band gap, thickness and charge carrier concentration in CuO equal to 1.4 eV, 3 μm and 1017 cm–3, as well as a displacement of the conduction band edges of 0.03 eV. The obtained results of modeling solar cells can be used in the design and manufacture of inexpensive and efficient photovoltaic structures.

Effect of Vertical Aspect Ratio on Mass, Stiffness and Vertical Geometric Irregular Structures

Occurrence of earthquake will highly damage the structures which are not modelled and designed to resist such loads it may cause sometimes the collapse of structures leads to loss of human lives. The size of the building and slenderness ratio are the important factors for the efficient structural design. The aspect ratio of buildings is considerably effects under seismic loads. It is important to know the influence of aspect ratio on the irregular structures in high seismic zone areas. The research has been performed on the 15 storey building frames of five cases with mass, stiffness and vertical geometric irregularities. Each case comprises of four models having various vertical aspect ratios. The purpose of this study is to investigate the influence of vertical aspect ratio on mass, stiffness and vertical geometric irregular structures and how the structure will vary under seismic loads. The irregularities are incorporated in to the buildings by increasing the loads and column height in particular storeys and by eliminating the columns and beam sections compared to adjacent storeys. Linear dynamic analysis is performed. It is observed that with the decrease of vertical aspect ratio the structural response will increases the response of storey displacement, base shear, storey drift and over turning moment in the cases of mass and stiffness irregular structures. In the case of vertical geometric irregular structures with the decrease of vertical aspect ratio the storey displacement and storey drift values decrease for all the models, the base shear, overturning moment and stiffness increases with the decrease of vertical aspect ratio. Key words: Mass irregularity, Stiffness irregularity, Vertical geometric irregularity, Vertical aspect ratio, Linear dynamic analysis.

Phosphorus‐Enhanced Bimetallic Single‐Atom Catalysts for Hydrogen Evolution

AbstractSingle‐atom catalysts (SACs), where individual metal atoms are anchored on support, hold great promise for electrocatalytic hydrogen evolution reactions (HERs). The inherent simplicity of the single‐atom center restricts opportunities for further enhancement. Binuclear SACs, incorporating two different metal sites, can further improve HER kinetics. However, the underlying mechanisms of the HER at the binuclear sites are complex and not fully understood, hampering the design of new efficient catalyst structures. Here, a comprehensive investigation is presented into phosphorus‐doped iron and cobalt bimetallic SACs supported on nitrogen‐doped graphitic carbon (P/FeCo‐NC), focusing on the potential mechanisms underpinning their enhanced HER activity. P/FeCo‐NC exhibits overpotentials of 38 and 95 mV at 10 mA cm2 in 0.5 m H2SO4 and 1 m KOH, respectively, nearing the acidic performance of commercial Pt/C (33 mV). Theoretical studies reveal that the phosphorus bridge significantly alters the electronic properties of the Fe active sites, while the adjacent Co atoms modulate the electronic environment, further optimizing the hydrogen adsorption‐free energy toward more favorable kinetics. This work highlights the structure‐activity relationships of bimetallic SACs and opens perspectives for future applications.

Nonlinear analysis of spatial trusses with different strain measures and compressible solid

This paper investigates the nonlinear behavior of spatial truss elements under finite deformations, focusing on the impact of various strain measures in compressible materials. We examine both Total Lagrangian (using engineering and Green–Lagrange strains) and Eulerian formulations (using natural, Biot, and Almansi strains). The analysis assumes a linear spatial hyperelastic material where Cauchy stress is proportional to axial natural strain via Young’s modulus. For infinitesimal strains, Young’s modulus remains consistent across different stress/strain pairs. In the finite strain regime, we derive a nonlinear secant modulus based on Young’s modulus. Internal force vectors and tangent stiffness matrices are computed using the direction cosines of the truss element in its deformed state. The paper demonstrates that for infinitesimal deformations, adjusting the modulus of elasticity when using different stress/strain pairs is unnecessary. However, for finite deformations, it is essential to adjust the modulus of elasticity. Numerical simulations validate the performance of the proposed 3D truss element against established formulations. This research offers critical insights into the nonlinear response of spatial trusses, guiding the selection of appropriate strain measures for enhanced accuracy in engineering applications. These findings contribute to more reliable and efficient structural designs, especially in scenarios involving finite deformations and compressible materials.

Study of the influence of macro-structure and micro-structure on the mechanical properties of stag beetle upper jaw

Macrostructural control of stress distribution and microstructural influence on crack propagation is one of the strategies for obtaining high mechanical properties in stag beetle upper jaws. The maximum bending fracture force of the stag beetle upper jaw is approximately 154, 000 times the weight of the upper jaw. Here, we explore the macro and micro-structural characteristics of two stag beetle upper jaws and reveal the resulting differences in mechanical properties and enhancement mechanisms. At the macroscopic level, the elliptic and triangular cross-sections of the upper jaw of the two species of stag beetles have significant effects on the formation of cracks. The crack generated by the upper jaws with a triangular section grows slowly and deflects easily. At the microscopic level, the upper jaw of the two species is a chitin cross-layered structure, but the difference between the two adjacent fiber layers at 45° and 50° leads to different deflection paths of the cracks on the exoskeleton. The mechanical properties of the upper jaw of the two species of stag beetle were significantly different due to the interaction of macro-structure and micro-structure. In addition, a series of bionic samples with different cross-section geometries and different fiber cross angles were designed, and mechanical tests were carried out according to the macro-structure and micro-structure characteristics of the stag beetle upper jaw. The effects of cross-section geometry and fiber cross angle on the mechanical properties of bionic samples are compared and analyzed. This study provides new ideas for designing and optimizing highly loaded components in engineering. Statement of significanceThe upper jaw of the stag beetle is composed of a complex arrangement of chitin and protein fibers, providing both rigidity and flexibility. This structure is designed to withstand various mechanical stresses, including impacts and bending forces, encountered during its burrowing activities and interactions with its environment. The study of the upper jaw of the stag beetle can provide an efficient structural design for engineering components that are subjected to high loads. Understanding the relationship between structure and mechanical properties in the stag beetle upper jaw holds significant implications for biomimetic design and engineering.

Cyclic inelastic performance evaluation of locking bolt demountable shear connectors in steel-concrete composite structures

This study extends the research on the Locking Bolt Demountable Shear Connector (LBDSC), previously introduced and extensively analysed under monotonic loading through both experimental and numerical methods. The focus here shifts towards the inelastic cyclic performance of the LBDSC, a critical aspect for applications in seismic regions. The LBDSC, designed for use in composite floors or decks, such as in-situ solid slabs, profiled deck slabs, or precast slabs in buildings and bridges, features a grout-filled steel tube bolted to the top flange of steel sections. The design incorporates a unique 'locking bolt' technique to counteract the influence of construction tolerances on undesirable initial slip behaviour, significantly facilitating the deconstruction and reuse of steel sections without the need for recycling. Following the Eurocode 4 guidelines, a series of standard push out tests were conducted to explore the LBDSC's inelastic cyclic characteristics. The experimental results confirm the LBDSC's capability for high strength, high initial stiffness, and adequate slip capacity under cyclic loading, with ductile shear failure modes observed at the bolt shank and local concrete crushing above the steel flange. The cyclic loading tests reveal a larger zone of damaged concrete compared to monotonic loading conditions, yet the monotonic and cyclic force-slip curves closely align. Based on these findings, and supported by validated finite element analyses, this paper proposes theoretical monotonic and cyclic models for LBDSC under push out loading, aimed at accurately predicting the load-slip response. These models are intended to facilitate the efficient analyses, design and numerical modelling of composite structures incorporating LBDSCs, and to support the broader adoption of LBDSCs in earthquake-prone areas, promoting sustainable steel-concrete composite construction practices.

Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence

Intelligent design technology for shear wall structures has great potential for enhancing design efficiency and addressing the challenges of tedious and repetitive design tasks. Recently, there has been a surge in the development of this technology. However, existing deep learning-based design methods for shear wall structures suffer from poor quality and usability issues. To address these challenges, this study proposes an intelligent design and optimization system for shear wall structures based on large language models (LLMs) and generative artificial intelligence (AI). The system employs an LLM as the core controller, which interacts with engineers to interpret their language descriptions and translate them into executable computer code. Subsequently, the system utilizes the corresponding structural generation and optimization methods to accomplish intelligent design tasks automatically. Furthermore, the system incorporates such critical factors as the empirical rules, mechanical performance, and material consumption into the structural optimization process. A unique three-level, two-stage optimization method is constructed based on topology, pattern, and size to enhance the overall design quality. Being able to complete the entire workflow of architectural drawing processing, structural scheme generation, and analysis model establishment, the proposed system enables automated and efficient design of shear wall structures. Through the analysis and validation of multiple cases, it was demonstrated that this system can significantly speed up the design by approximately 30 times compared to that of traditional methods whilst ensuring the safety and cost-effectiveness of the design schemes. Consequently, this study provides valuable insights for the advancement of automated structural design undertakings.

Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers

AbstractIn recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.

Optimization of Hanger Spacing of Steel Arch Bridges Using Dynamic Loads

Bridges are basic infrastructure that must be met to create regional connectivity in Indonesia. One type of bridge that is often used is a curved bridge, which has the advantages of high strength, attractiveness, aesthetics and economy. In order to accelerate the development of bridge infrastructure, an efficient innovation in curved bridge design is needed. The development of curved bridge structures to achieve efficient designs has received much attention in several decades. However, researchers have only focused on optimising the geometry variation of the arch height. Therefore, the aim of this research is to innovate the optimisation of the hanger spacing on the arch bridge structure. In order to obtain optimal results, a bridge model is carried out by varying the hanger spacing of the centre model with a hanger spacing ratio of (1.3 - 1.1 - 0.9 - 0.7), a flat model with a hanger spacing ratio of (1 - 1 - 1 - 1) and an edge model with a hanger spacing ratio of (0.7 - 0.9 - 1.1 - 1.3), so that from the three models, the effect of hanger location on three conditions is obtained. Each model is modelled in the SAP2000 software and given a bridge service load to obtain the internal forces and deflections that occur. The output of the internal force and deflection is then analysed to determine the effect of the location of the bridge service hanger. The serviceability of the bridge is also analysed by calculating the ratio between the weight of the bridge and the deflection that occurs. The results of the analysis show that the location of the hanger affects the performance of the arch bridge structure. The centre model bridge design produces the most efficient structural performance in resisting the compressive axial forces and moments that occur, and produces the least deflection. Meanwhile, the edge model will provide the most efficient structural performance in resisting tensile axial forces. By referring to the results of the bridge weight to deflection ratio analysis, it can be concluded that the centre model produces the most efficient structural design when compared to other curved bridge models.

Breaking Wave Statistics In Short-Crested Seas

The accurate prediction of loads to allow for a safe yet efficient design of coastal and offshore structures requires a thorough understanding of the distribution of crest heights. Although deep water waves are relatively well understood and accurately modelled by existing wave and crest height models, the same cannot be said for waves in intermediate and shallow waters (Tayfun & Alkhalidi, 2020). This work addresses this gap by analysing long, random records of laboratory-generated short-crested seas. The analysis focuses on the application of a novel method to identify waves that undergo significant nonlinear amplification and breaking. Following their identification, the associated energy transfers and dissipation are calculated per wave and probabilistically approximated. A modelling suite is proposed to describe the probability of wave breaking and associated dissipation in short-crested seas. This is then converted into a mixture model to recover crest height statistics. The success of the proposed approach is demonstrated through comparisons between model predictions and measurements.

EXPERIMENTAL AND NUMERICAL STUDY OF INCREASED CORE SURFACE AREA ON THE PERFORMANCE OF ADDITIVELY MANUFACTURED HONEYCOMB SANDWICH STRUCTURES

Abstract Sandwich composite structures have an efficient structural design that provides high bending stiffness and strength at low weight. However, these structures are plagued with weak core-to-facesheet bond strength. In this work, the effect of increasing the contact area between the composite facesheet and honeycomb core was studied. Sandwich panels were manufactured using two different honeycomb structures, a regular and a modified honeycomb, and their respective bond strengths were evaluated using the flatwise tension test (ASTM C297). The honeycombs were additively manufactured using selective laser melting (SLM) process. The modified honeycomb was designed to have a larger surface area while retaining the same relative density as the regular honeycomb. The facesheets were made out of carbon-fiber, while the core was additively manufactured using 304L stainless steel powder. Impact specimens were manufactured and tested for impact resistance. A finite element model was created to study the integrity of the sandwich structures subjected to localized impact damage. The finite element model of the damage resistance due to impact showed a good agreement with the experimental results. Samples with increased contact area showed higher impact resistance. The average impact strength of the modified samples was 41.3% higher than the average impact strength of the regular samples. Flatwise tension results showed that by increasing the contact area between the core and the facesheet the core-to-facesheet bond strength increased.

Stress-driven generative design and numerical assessment of customized additive manufactured lattice structures

The rise of additive manufacturing (AM) has positioned lattice infilling as a pivotal strategy for creating lightweight, customized engineering components. This study presents a generative method that enables the conformal design and stiffness prediction of complex gradient strut-node lattice structures. A stress-driven Multi-Agent System (MAS) is introduced for the parametric optimization of lattice material distribution, incorporating geometric limitations, stress factors, and AM constraints. A beam element model simplifies the numerical analysis of the structure linear stiffness. By applying the Response Surface Method (RSM), a numerical model is established, not only conducting a quantitative analysis on the sensitivity of MAS design variables but predicting mechanical performance. This method is validated by designing a supporting component, demonstrating that the optimized lattice design can achieve a linear stiffness 1.4 times greater than that of conventional uniform lattice infills for the same mass. This research provides a comprehensive framework for the efficient design and analysis of irregular lattice structures at a macroscopic scale.

The engineering legacy of the FIFA World Cup Qatar 2022TM: Al Bayt Stadium

In contemporary architecture, textile membranes offer a wide range of possible structural applications, especially for facades and roofs with large spans and where the reduction of self-weight plays an important role for an efficient and sustainable structural design. On 30 November 2021, the Al Bayt Stadium was inaugurated in Qatar; with its 190 000 m2 of textile membrane it is one of the biggest architectural membrane projects in the world. An innovative new membrane material with a natural textile appearance was developed from scratch to meet the specific requirements of the project. This makes the construction of Al Bayt Stadium, shaped like the local Bedouin tents, a milestone in modern membrane architecture; this is attributable not only due to the pure size of the membrane, but also to the significant advances made in textile membrane material design and development as part of the project. Other design aspects include the large, retractable roof above the pitch, which transforms the open-air stadium into a fully enclosed and temperature-controlled multi-purpose arena within minutes. This paper presents details of the design and construction aspects of the stadium, as well as the material development conducted for the inner membrane.

In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design

Using modern machines like robots comes with its set of challenges when encountered with unstructured scenarios like occlusion, shadows, poor illumination, and other environmental factors. Hence, it is essential to consider these factors while designing harvesting robots. Fruit harvesting robots are modern automatic machines that have the ability to improve productivity and replace labor for repetitive and laborious harvesting tasks. Therefore, the aim of this paper is to design an improved orange-harvesting robot for a real-time unstructured environment of orchards, mainly focusing on improved efficiency in occlusion and varying illumination. The article distinguishes itself with not only an efficient structural design but also the use of an enhanced convolutional neural network, methodologically designed and fine-tuned on a dataset tailored for oranges integrated with position visual servoing control system. Enhanced motion planning uses an improved rapidly exploring random tree star algorithm that ensures the optimized path for every robot activity. Moreover, the proposed machine design is rigorously tested to validate the performance of the fruit harvesting robot. The unique aspect of this paper is the in-depth evaluation of robots to test five areas of performance that include not only the accurate detection of the fruit, time of fruit picking, and success rate of fruit picking, but also the damage rate of fruit picked as well as the consistency rate of the robot picking in varying illumination and occlusion. The results are then analyzed and compared with the performance of a previous design of fruit harvesting robot. The study ensures improved results in most aspects of the design for performance in an unstructured environment.

Deciphering Structure-Activity Relationship Towards CO2 Electroreduction over SnO2 by A Standard Research Paradigm.

Authentic surface structures under reaction conditions determine the activity and selectivity of electrocatalysts, therefore, the knowledge of the structure-activity relationship can facilitate the design of efficient catalyst structures for specific reactivity requirements. However, understanding the relationship between a more realistic active surface and its performance is challenging due to the complicated interface microenvironment in electrocatalysis. Herein, we proposed a standard research paradigm to effectively decipher the structure-activity relationship in electrocatalysis, which is exemplified in the CO2 electroreduction over SnO2 . The proposed practice has aided in discovering authentic/resting surface states (Sn layer) of SnO2 accountable for the electrochemical CO2 reduction reaction (CO2 RR) performance under electrocatalytic conditions, which then is corroborated in the subsequent CO2 RR experiments over SnO2 with different morphologies (nanorods, nanoparticles, and nanosheets) in combination with in situ characterizations. This proposed methodology is further extended to the SnO electrocatalysts, providing helpful insights into catalytic structures. It is believed that our proposed standard research paradigm is also applicable to other electrocatalytic systems, in the meantime, decreases the discrepancy between theory and experiments, and accelerates the design of catalyst structures that achieve sustainable performance for energy conversion.

Deciphering Structure‐Activity Relationship Towards CO2 Electroreduction over SnO2 by A Standard Research Paradigm

AbstractAuthentic surface structures under reaction conditions determine the activity and selectivity of electrocatalysts, therefore, the knowledge of the structure‐activity relationship can facilitate the design of efficient catalyst structures for specific reactivity requirements. However, understanding the relationship between a more realistic active surface and its performance is challenging due to the complicated interface microenvironment in electrocatalysis. Herein, we proposed a standard research paradigm to effectively decipher the structure‐activity relationship in electrocatalysis, which is exemplified in the CO2 electroreduction over SnO2. The proposed practice has aided in discovering authentic/resting surface states (Sn layer) of SnO2 accountable for the electrochemical CO2 reduction reaction (CO2RR) performance under electrocatalytic conditions, which then is corroborated in the subsequent CO2RR experiments over SnO2 with different morphologies (nanorods, nanoparticles, and nanosheets) in combination with in situ characterizations. This proposed methodology is further extended to the SnO electrocatalysts, providing helpful insights into catalytic structures. It is believed that our proposed standard research paradigm is also applicable to other electrocatalytic systems, in the meantime, decreases the discrepancy between theory and experiments, and accelerates the design of catalyst structures that achieve sustainable performance for energy conversion.

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

Enhancing Structural Resilience: A Comparative Study of G+35 Storey Irregular Buildings with Bracing and Diagrid Systems

Abstract: The study presented herein delves into the structural performance and behavior of a building subjected to different configurations, namely, an Irregular Building, an Irregular Building with Steel Bracing, and an Irregular Building with Diagrid System. The focus of the investigation is on key structural parameters such as node displacements and storey shear values across various levels of the building. Understanding how these parameters vary in response to different design strategies is vital for assessing the overall stability, safety, and efficiency of the structure in the face of external forces. Structural engineers and designers are constantly striving to optimize building designs to meet safety standards and performance criteria. The incorporation of additional elements, such as steel bracing or a diagrid system, can significantly impact a structure's ability to withstand lateral forces, ensuring resilience in the face of dynamic loads such as wind or seismic events. By comparing the performance of the three configurations, this study aims to provide insights into the efficacy of these design strategies and their influence on key structural indicators. Through a comprehensive analysis of node displacements and storey shear values at various levels, this study seeks to contribute valuable information to the field of structural engineering. The findings may inform future design decisions, guide improvements in structural systems, and enhance the understanding of how different configurations influence the overall behavior of a building. Ultimately, the knowledge gained from this study can contribute to the advancement of resilient and efficient structural designs, with implications for the broader fields of architecture and civil engineering.

An Accurate Prediction Method of Human Assembly Motion for Human–Robot Collaboration

In the process of human–robot collaborative assembly, robots need to recognize and predict human behaviors accurately, and then perform autonomous control and work route planning in real-time. To support the judgment of human intervention behaviors and meet the need of real-time human–robot collaboration, the Fast Spatial–Temporal Transformer Network (FST-Trans), an accurate prediction method of human assembly actions, is proposed. We tried to maximize the symmetry between the prediction results and the actual action while meeting the real-time requirement. With concise and efficient structural design, FST-Trans can learn about the spatial–temporal interactions of human joints during assembly in the same latent space and capture more complex motion dynamics. Considering the inconsistent assembly rates of different individuals, the network is forced to learn more motion variations by introducing velocity–acceleration loss, realizing accurate prediction of assembly actions. An assembly dataset was collected and constructed for detailed comparative experiments and ablation studies, and the experimental results demonstrate the effectiveness of the proposed method.