Engineering Structures Research Articles

Enhanced Hg(II) reduction activity over two-dimensional n-n heterojunction MoS2/Bi2WO6 nanocomposites under visible light illumination

The engineering of band structure acts as an effective avenue to promote photocatalytic redox reactions, which can be achieved by fabricating heterojunction photocatalysts. Herein, MoS2 NPs were anchored on the surface of the mesoporous Bi2WO6 network through a facile sol-gel process to form heterostructure MoS2/Bi2WO6 nanocomposites. HR-TEM and XRD results of the obtained MoS2/Bi2WO6 nanocomposite verified the formation of the hexagonal phase of MoS2 and the Bi2WO6 in the orthorhombic phase. The MoS2/Bi2WO6 nanocomposites displayed enhancement harvest in the visible light domain and photocatalytic ability in comparison with the pristine Bi2WO6. The photocatalytic ability of MoS2/Bi2WO6 nanocomposites was assessed by the Hg(II) reduction upon visible light irradiation. The optimal 12% MoS2/Bi2WO6 photocatalyst displayed the superior reduction of Hg(II) about 99% after 50 min, and the rate constant reached 0.1220 min-1 according to pseudo-first-order kinetic, where it was larger three folds than pristine Bi2WO6 (0.0418 min-1). The outstanding photocatalytic ability of n-n heterojunction MoS2/Bi2WO6 photocatalyst was accredited to the huge surface area, wide photoabsorption, and high separation efficiency of photocarriers. The reduction ability of MoS2/Bi2WO6 photocatalyst remained at 94% in the recycle five times, indicating its good reusability and high stability. The obtained heterojunction with the S-scheme mechanism could inhibit the recombination of the photocarriers, thus leading to superb photocatalytic ability. This work emerges an efficient route to design S-scheme heterojunction Bi2WO6-based photocatalytic methods for energy conversion and environmental purification.

Low-Head Hydraulic Structures in Irrigation and Drainage Engineering: Challenging Operation and Design Implications

Low-Head Hydraulic Structures in Irrigation and Drainage Engineering: Challenging Operation and Design Implications

Simultaneous structure and surface engineering of metal-organic framework derived LiNi0.5-xFe2xMn1.5-xO4 cathode material for improving high voltage performance of lithium-ion batteries

This study presents a novel material synthesis approach utilizing a terephthalic acid-based metal-organic framework as a precursor and employing an ethanol-assisted hydrothermal treatment to produce high-performance Fe-doped LiNi0.5Mn1.5O4 (LNMO) cathode material. This strategy yields a well-crystallized spinel structure with uniformly distributed transition metal ions. Additionally, the appropriate quantity of Fe dopant can achieve the desired Mn3+/Mn4+ ratio, level of structural disordering, and crystal phase purity for Fe-doped LNMO. The synthesis process also aids in forming an amorphous Li2CO3 surface layer, approximately 1 nm thick, which protects the active material from excessive reaction with electrolyte. The typical Fe-doped LNMO cathode (S-05) exhibits superior performance compared to a commercialized LNMO counterpart. It delivers an initial discharge capacity of 135 mAh g-1 at 1 C with exceptional cycling stability over 500 cycles (capacity retention of 90%) under high voltage (5.0 V vs. Li+/Li). Furthermore, its rate capability significantly improves, with a capacity retention of 85% (5 C/0.2 C). This enhanced performance aligns with evidence of activated Ni2+/Ni3+/Ni4+ redox reactions and suppressed cathode electrolyte interphase resistance, leading to promoted Li+ diffusion. Moreover, it is found the cathode helps alleviate electrolyte degradation at high voltages by inhibiting the formation of singlet oxygen in the electrolyte.

Tailoring optical and electrical properties of PVP-I2 complexes through gamma irradiation-induced structural defect Engineering for optoelectronic applications

This study investigates the complexation of iodine with polyvinylpyrrolidone (PVP) subjected to gamma irradiation at doses of 0, 10, 20, and 30 kGy. The complex formation involves I2 acting as a Lewis acid with electron-rich PVP as a Lewis base through a charge transfer mechanism. UV–visible spectroscopy revealed a red shift of the absorption peak at 337 nm for irradiated PVP-I2 samples, indicating stronger interactions between the triiodide ion (I3¯) and irradiated PVP. FTIR analysis confirmed the formation of new C≡N moieties arising from PVP-I2 complexation. The FT-Raman analysis revealed a new C≡N peak at 2360 cm−1 in irradiated PVP-I2 samples, with increasing intensity correlating to higher irradiation doses, indicating enhanced electron delocalization due to complex formation. XRD analysis showed that PVP-I2 samples irradiated at 0 and 10 kGy exhibited higher crystallinity than those irradiated at 20 and 30 kGy, suggesting that high-dose irradiation causes PVP radiolysis and ring opening. Dielectric measurements demonstrated enhanced polarizability and ionic conductivity of irradiated PVP-I2 films, related to structural disorder promoting charge dissociation and ion mobility. The direct bandgap decreased from 2.75 eV for unirradiated samples to 2.55 eV for samples irradiated at 30 kGy, indicating the creation of new optically active defect states. Urbach energy increased from 0.85 eV to 1.55 eV with increasing irradiation dose, confirming increased disorder in the PVP structure. These findings reveal that gamma irradiation significantly alters the optical and electrical properties of PVP-I2 films by creating defects and disrupting structure, offering new insights for tailoring these materials for optoelectronic applications.

Reliability-based safety format for structural fire engineering – Derivation based on the most likely failure point

Designing structures for burnout resistance ensures stability during evacuation and search and rescue operations, limits collateral damage, and enhances post-fire repairability. This represents a significant shift from traditional prescriptive designs that do not evaluate performance under realistic fire conditions. However, given the variability in fire exposure and structural response, it is unclear which input values should be used to ensure a high level of reliability for burnout calculations. This paper introduces a safety format for burnout resistance compatible with the Eurocode and its reliability principles. The format allows users to specify desired reliability levels and prescribes equations for determining design values for load effects and fire load density using predetermined sensitivity weights. A method for calculating default sensitivity weights is outlined, proposing tentative values: 0.65 for resistance effect, -0.40 for load effect, and -0.80 for fire load density, with a default coefficient of variation of 0.30 for resistance effect when case-specific information is lacking. The safety format's performance is verified through case studies of a concrete slab and a numerical evaluation of a steel column, showing satisfactory and conservatively assessed results. Inherent conservatism in the design format may, however, occasionally lead to the undue rejection of designs. Further investigations are necessary to confirm the safety format's conceptualization, default sensitivity weights, and the influence of the adopted compartment fire model.

Editorial

The IOP Conference Series: Materials Science and Engineering journal contains the peer-reviewed papers presented at the 10th International Conference on Advanced Materials and Structures - AMS 2024. The conference took place between 30.05 to 01.06 2024 at Timisoara, Romania and it was the tenth edition of the scientific event organized by the Department of Materials Engineering and Manufacturing and Department of Mechanics and Strength of Materials from University Politehnica of Timisoara with the support of the Politehnica Foundation, Technical Sciences Academy of Romania and Romanian Academy - Timisoara Branch. Additionally, this tenth edition continues the tradition of previous AMS conferences by providing a platform for researchers, academic staff, young PhD students, and industrial partners to present their scientific achievements in the field of: - Advanced materials-amorphous, nano-structured materials, composites, cellular materials, biomaterials etc.; - Surface engineering; - Modern fabrication, additive manufacturing, joints and recycling technologies; - Materials damage under time-dependent-actions (fatigue, creep, impact, corrosion); - Computational techniques for advanced engineering materials and structures. We hope that our readers find this special issue both interesting and useful. Guest editors Prof. Viorel-Aurel ȘERBAN, University Politehnica of Timisoara, Romania Prof. Liviu MARȘAVINA, University Politehnica of Timisoara, Romania Prof. Ion-Dragoș UȚU, University Politehnica of Timisoara, Romania Assoc.Prof. Emanoil LINUL, University Politehnica of Timisoara, Romania

Efficient and durable OER electrocatalysts through vacancy engineering and core-shell structure design in acidic environment

The electrocatalytic conversion of water into green hydrogen energy through overall water splitting advances sustainable energy goals. However, it is limited by the slow kinetics of the oxygen evolution reaction (OER) and durability challenges at high potentials. Here, we rationally develop a series of Mn-doped RuO2-coated Ru (Mn-Ru@RuO2) catalysts involving unique core-shell structures and abundant lattice distortions induced by oxygen defects. The optimal Mn-Ru@RuO2 sample demonstrated robust activity, achieving a low overpotential of 220 mV at 10 mA cm−2. Notably, this sample exhibited minimal degradation after 220 h and a 25.5-fold increase in mass activity (1.37 A mgRu−1) compared to commercial RuO2 at an overpotential of 300 mV. These results suggest that regulated oxygen deficiencies and optimal Ru3+/Ru4+ ratios facilitate the lattice oxygen oxidation (LOM) mechanism and the formation of a high concentration of ∗OH radicals, enhancing acidic OER performance. Additionally, the unique core-shell structures effectively prevent over-oxidation of the RuO2 shell, ensuring superior durability. This research not only breaks the trade-off of electrochemical activity and durability, but also provides valuable insights into the design of highly efficient and stable electrocatalysts.

IABSE Congress Ghent 2025—The Essence of Structural Engineering for Society, 27–29 August 2025

IABSE Congress Ghent 2025—The Essence of Structural Engineering for Society, 27–29 August 2025

Nanometer-Resolved Mapping of Organic Cation Migration Behavior in Methylammonium Lead Halide Perovskites.

The performance and stability of organic metal halide perovskite (OMHP) optoelectronic devices have been associated with ion migration. Understanding of nanoscale resolved organic cation migration mechanism would facilitate structure engineering and commercialization of OMHP. Here, we report a three-dimensional approach for in situ nanoscale infrared imaging of organic ion migration behavior in OMHPs, enabling to distinguish migrations along grain boundary and in crystal lattice. Our results reveal that organic cation migration along OMHP film surface and grain boundaries (GBs) occurs at lower biases than in crystal lattice. We visualize the transition of organic cation migration channels from GBs to volume upon increasing electric field. The temporal resolved results demonstrate the fast MA+ migration kinetics at GBs, which is comparable to diffusivity of halide ions. Our findings help understand the role of organic cations in the performance of OMHP devices, and the proposed approach holds broad applicability for revealing migration mechanisms of organic ions in OMHPs based optoelectronic devices.

Sustainable development of agro bio-mass waste based AA6061-Tungsten carbide hybrid reinforced composites: A comprehensive performance investigation

The Recycling, Reusing, and Reduction (3R) strategy to solid waste management is proposed for exploiting the functionalities of wastes in enhancing base material performance, which could contribute to produce a sustainable product and environment. This study investigates the impact of organic and synthetic ceramic reinforcements; coconut shell ash (CSA), and tungsten carbide (WC) at various concentrations (1, 2, and 3 wt. % each) on microstructural and performance characteristics of sustainable AA6061 based hybrid composites. The composites are produced via the stir casting process with mechanized stirring. The microstructural characteristics of the produced aluminium hybrid composites (AMHC) are evaluated by FESEM/EDAX, FT-IR and XRD studies to examine their elemental composition, morphology and crystalline nature. The mechanical properties are investigated by charpy impact, tensile, compressive and micro-harness studies. The density and porosity analysis are employed to ascertain their physical characteristics. Morphology investigations show that the selected reinforcing materials are present and uniformly dispersed across the matrix's surface. The produced composite with 4 wt. % of CSA and WC reinforcements has the ability to improve the hardness (75.5 %), tensile strength (42.87 %), and compressive strength (55.71 %) of the base Aluminium alloy. Overall, using organic and synthetic ceramic substances as reinforcements for AA6061 alloy can result in sustainable high-performance composites for structural engineering applications across fields.

Numerical assessment of thermal bridging effects in 3D-printed foam concrete walls

Abstract Integrating smart technology and advanced materials in the construction industry has revolutionized traditional building practices, enhancing efficiency, sustainability, and overall performance. Researchers and professionals in the construction sector have shown significant interest in three-dimensional concrete printing (3DCP) for automating structural engineering tasks. Despite its potential as a sustainable solution to modern construction issues, there is a lack of research on the thermal insulation performance of three-dimensional printed concrete (3DPC) building envelopes, and the potential for integrating foam concrete (FC) to enhance energy efficiency has not yet been studied. This paper presents a numerical analysis examining how different infill geometries affect the thermal performance of 3D-printed foam concrete (3DPFC) lattice envelopes. Six lattice structures were designed with identical thickness, height, length, and comparable insulation areas. The effects of the contact (intersection) area of webs with the interior face shell, webs, and infill rows on the thermal performance of granularly insulated envelopes were studied. The effectiveness of insulation was also established. The findings indicate that the thermal transmittance of 3DPC envelopes correlates directly with the contact area of the webs and the interior surface, with U-values ranging from 0.151 W m2·K to 0.652 W/m2·K. Notably, the absence of direct connections between exterior and interior surfaces enhances insulation efficiency, with double-row structures achieving up to 94% insulation efficiency. However, when there is a direct connection between the two surfaces, the thermal performance of these envelopes is mainly affected by the contact (intersection) area of the webs with the interior face rather than the number of webs. By integrating foam concrete and double-row walls, this study demonstrates an innovative approach to reducing thermal bridging and improving energy performance in 3D-printed construction. The results offer novel insight into optimizing the thermal behavior of 3DPC systems for sustainable building practices.

Development of Johnson-Cook-Distinct Lattice Spring Model and its application in projectile penetration into metal targets

Projectile penetration into metal targets plays an important role in modern protective structure engineering, damage assessment in terrorist attacks, and car clash accident analysis, etc. The penetration process of metal target has been characterized by large deformation, high temperature, high pressure, and dynamic damage, which can be classified as a continuous-discontinuous dynamical process. To capture the dynamic responses of metal materials subjected to high-speed penetration, Johnson-Cook model has been implemented in a continuum-discrete model, namely, Distinct Lattice Spring Model (DLSM). This is achieved by redefining the constitutive model in DLSM, with plasticity hardening (plastic strain effects), strain rate, temperature, damage evolution, equation of state, etc., being taken into account, thus developing a new numerical model called JC-DLSM model. This model is validated through studying Taylor rod high-speed impact experiments, thin metal plate penetration experiments, and projectile impact experiments on titanium alloy targets. Good agreement between numerical modelling and experimental data has been achieved for all cases considered, thereby demonstrating the capability of JC-DLSM to reproduce the damage characteristics and ballistic limits of metals under high-speed impact/penetration. Then, the impacts of projectile shape, metal target surface configuration on the characteristics of penetration damage patterns have been investigated. This contributes to a better understanding of the damage mechanisms of metal targets upon penetration or impact, facilitating the computational means for analysing and optimizing the penetration resistance of protective engineering structures.

Synergistic charge-transfer dynamics of novel benzothiadiazole-based donor materials for higher power conversion efficiency: From structural engineering to efficiency assessment in non-fullerene organic solar cells

In the realm of organic solar cell technology, current research is dedicated to enhancing the photovoltaic properties of donor-π-acceptor (D-π-A) materials to achieve higher power conversion efficiencies (PCE). This optimization focuses particularly on fine-tuning the conduction band and electrolytic characteristics to maximize performance. Addressing the growing demand for novel materials with enhanced optoelectronic properties in organic photovoltaic research, our proposed compound BT05, one of nine new benzothiadiazole-based D-π-A donor molecules (BT01-BT09), exhibits a power conversion efficiency (PCE) of 25%, surpassing the 18% PCE of the reference compound BTD-OMe. TD-DFT and DFT simulations illuminate how donor modifications enhance the photovoltaic characteristics of the proposed molecules. Higher open-circuit voltage (VOC) of 1.74-2.26 V, increase in binding energy (∼1.997), λmax (470-476 nm), reduction in energy gap (4.25-4.65 eV), also validates the PCE results and confirm the usefulness of designed molecules (BT01-BT09). Moreover, DHOMO and ALUMO, TDM, reorganization energy λe (0.0124-0.0134) and λh (0.0094-0.0098), and NPA results also confirm that BT01-BT09 molecules unlock the organic solar cell's potential and advance sustainable energy solutions through innovative technology. Among all developed compounds, BT05 displays higher VOC (2.26 V), 87% fill-factor, and 25% PCE; hence, it is recommended in future solar cell applications.

Efficient deep learning based models for rapid prediction of RC shear wall responses under monotonic and cyclic loading

The seismic response of reinforced concrete (RC) shear walls is of paramount importance in earthquake-resistant design. While traditional techniques such as finite-element-based modeling and experimental testing provide comprehensive insights into shear wall behavior, they often require significant computational resources and time. In response, this study investigates the efficacy of various Deep Neural Networks (DNNs), including Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and LSTM-Based Autoencoders, in predicting the behavior of RC shear walls under monotonic and cyclic loading conditions. Through training on a diverse dataset encompassing various geometric configurations, material properties, and loading scenarios, the proposed approach generalizes well to unseen cases, enabling rapid estimation of shear wall responses without extensive numerical simulations. This approach offers several advantages, including faster design iterations, improved assessment of existing structures, and potentially higher accuracy. Extensive comparative analyses against experimental tests and numerical simulations evaluate the performance of the deep learning models. Results demonstrate that the proposed approach achieves superior accuracy in prediction of hysteresis and pushover curves, effectively capturing the nonlinear structural behavior of RC shear walls subjected to static and cyclic loading conditions. Validation using a comprehensive experimental database of 160 specimens shows that the model achieves less than 10 % error in maximum lateral load predictions for specimens within the training data range; while simultaneously reducing computational time Overall, this study contributes to the evolving landscape of structural engineering by showcasing the potential of DNNs as efficient tools for predicting the behavior of RC shear walls.

Synthesis and characterization of new continuous phosphate glass fibers intended for structural engineering applications: Structure/property relationships

Nowadays, phosphate glass fibers (PGF) are considered quite competitive respective to conventional glass fibers. This work focus on the development of PGF fibers intended for structural engineering applications such as composite reinforcement for building and automotive fields. For this purpose, two series of phosphate glasses based on 52P2O5-24CaO-13MgO-(11-(X+Y+Z)) K2O-XAL2O3-YF2O3-ZTiO2; (X=1; 3; 5, Y=0; 5, Z=0; 1 mol %) were processed and transformed into phosphate glass fibers by melt spinning. The resulting fibers were characterized. XRD analysis confirmed the non-crystalline nature of phosphate glasses. In addition, the substitution of K2O by Al2O3 and by the combination of Al2O3, Fe2O3 and TiO2 in the composition of phosphate glass could lead to a significant increase in fiber density from 2,16 g/cm3 to 2,80 g/cm3. The stability of the produced phosphate glass fibers was examined using two methods: weight loss at 37°C and dissolution kinetics under different pH levels (4, 7, and 12). The results showed that the chemical resistance of the PGF fibers was improved with up to 99% increase respective to the original formulation. In addition, the mechanical properties of the spinnable phosphate glass manufactured by replacing K2O oxide by Al2O3 oxide were improved, such a substitution led to a maximum tensile strength and modulus of 2668 MPa and 140 GPa, respectively. Therefore, the tensile proprieties were improved by 75% compared to the original formulation. This comparative study between phosphate glass fibers (PGF) and traditional fibers highlight similar tensile strength but combined to notable enhancements in chemical stability through cation addition, expanding their potential use in composite and biomedical materials. Finally, a correlation analysis of mechanical performances was carried out. It was observed that the results obtained using the statistical methods were consistent with the experimental data.

Atomic-level structural engineering of La-doped CoMoP composite for enhanced overall water splitting

A series of La-doped CoMoP composites were fabricated through an in-situ phosphorization strategy with La–CoMo layered double hydroxide (LDH) as precursors. The homogenous dispersion of metal ions within the LDH structure facilitated the creation of a uniformly atomic-level La-doped CoMoP composite. Among these, the 5 % La-doped CoMoP exhibited the best performance, requiring only 41 mV overpotential for hydrogen evolution reaction (HER) and 303 mV for oxygen evolution reaction (OER) to reach a current density of 10 mA cm−2. For overall water splitting, it achieved a current density of 10 mA cm−2 at a cell voltage of just 1.61 V and maintained a stable performance over 24 h. The modulation effect of rare-earth elements, combined with the synergistic interactions among transition metal ions, contributes to its excellent bifunctional electrocatalytic performance.

Truss sizing optimum design using a metaheuristic approach: Connected banking system

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

A fast impact force identification method via constructing a dynamic reduced dictionary

Impact force identification is an important aspect of structural health monitoring for online assessment of the integrity of engineering structures. Increasing the number of monitoring positions is undoubtedly beneficial to accurate force localization, however, it may increase sharply the dimension of the transfer matrix and thus lead to a large-scale inverse problem. In this case, traditional methods, such as the Iterative Shrinkage Threshold algorithm (IST) and the Two-Step Iterative Shrinkage Thresholding algorithm (TwIST), suffer from low solving efficiency. In this paper, the dynamic reduced dictionary is firstly introduced according to the sparsity of impact force in both time and spatial domains so as to lower significantly the dimension of the transfer matrix. Then, a new reduced unconstrained optimization problem is established via the dynamic reduced dictionary for impact force identification. By integrating the reduced unconstrained optimization problem into each iteration step of IST/TwIST, two novel algorithms, which are the reduced Newton iteration shrinkage threshold algorithm (rNIST) and the reduced Newton two-step iteration shrinkage threshold algorithm (rNTwIST), are proposed to solve quickly the large-scale impact force identification problem using ℓ1-norm sparse regularization. Meanwhile, the adaptive setting of regularization parameters strategy is used to construct force more fast and accurately. The proposed algorithms are numerically and experimentally demonstrated through identifying impact forces at 16-point and 81-point distributions of a plate using one sensor response. The results show that rNIST/rNTwIST can more quickly and accurately identify impact forces under numerous monitoring points compared with other ℓ1-norm regularization methods and nonconvex sparse regularization methods. Moreover, the more the number of monitoring points, the more obvious the efficiency improvement of the impact force identification by rNIST/rNTwIST method.

Influence of different cooling methods on the creep characteristics and damage model of layered limestone after thermal treatment

In the actual underground energy extraction projects, the long-term stability of the well wall surrounding rock through the layered rock body often faces the influence of complex factors such as joints, thermal and cooling. The laminated limestone as a common rock type with obvious laminated structure and petroleum reservoirs, under high temperature environment can directly relate to the stability and safety of underground engineering structures. This paper investigates the creep characteristics of laminated limestone under high-temperature natural cooling and water cooling, analyzing the correlation between creep deformation, creep rate, long-term strength, and the angle of laminations, temperature, cooling mode. It then introduces a non-constant fractional-order cohesive body to construct an improved model based on these findings. The results indicate that under the same temperature and cooling method, as the lamination angle increases, the long-term strength shows a V-shaped trend, while the creep rate shows an inverted V-shaped trend. Specifically, the water cooling conditions at 500 °C (0°, 30°, 45°, 60°, and 90°) are 85.75 MPa, 71.24 MPa, 42.30 MPa, 61.52 MPa, and 87.40 MPa, respectively. For specimens with the same lamination angle, the creep mechanical properties of specimens cooled by water are more deteriorated than those cooled naturally. Taking the 100 °C action stratified limestone, long-term strength (0°, 30°, 45°, 60°, 90°) corresponding to natural cooling are 120.15 MPa, 106.12 MPa, 72.71 MPa, 91.43 MPa, and 123.32 MPa, respectively, while the long-term strengths corresponding to water cooling are 116.96 MPa, 100.97 MPa, 70.65 MPa, 85.34 MPa and 119.63 MPa, respectively. Finally, by comparing with the experimental results of nodular limestone under different cooling methods, it is found that the creep damage model proposed in this paper is not much different from the experimental curves, and it can effectively express the creep deformation and mechanical behavior of laminated limestone after high temperature. The results and findings are important for optimizing the design and prevention of potential underground energy extraction project disasters, and also provide important data support.

Digital twins for engineering structures—An Industry 4.0 perspective

AbstractThe term “digital twin” is becoming increasingly prevalent in both research and politics with regard to infrastructure structures. Industry is making significant advancements in the fields of automation and autonomy. While numerous definitions are in circulation, some of which are identical, many are similar in nature. This work examines the digital twin from its original industry perspective and considers its relevance to the constructional engineering sector. The objective of this study is to provide an overview of the current approaches in the Industry 4.0 and constructional engineering industry, examining the associated technical opportunities and challenges. The study compares the definitions, requirements and projects executed in this field. The technologies under examination are contrasted on the basis of their position within the data model. The preliminary findings suggest that some solutions for Industry 4.0 have already been developed for the construction engineering sector. However, it must be acknowledged that some solutions have yet to be fully validated.