This article presents a novel methodology for the simultaneous optimization of both structural topology and printing path in 3D concrete printing (3DCP), addressing a critical gap between digital design and physical manufacturability. Unlike conventional sequential approaches, our framework is grounded in discrete frame structures, which inherently reflect the filament-based nature of 3DCP, thereby enhancing geometric and mechanical fidelity. The proposed formulation strategically leverages the inherent anisotropy of printed concrete by aligning the printing direction along the longitudinal axis of each frame member to maximize strength and material efficiency. Key manufacturing constraints are integrated directly into the optimization process: member widths are restricted to integer multiples of the nozzle size, and the printing path is enforced as a globally continuous, non-intersecting, and non-overlapping Eulerian circuit through a mixed-integer linear programming model. The efficacy of this simultaneous optimization approach is demonstrated through a series of benchmark problems, which confirm that the resulting designs not only satisfy strict structural displacement and stress constraints with minimal material usage but are also readily manufacturable via direct "one-stroke" printing. This work establishes a foundational integration of structural performance and manufacturability, paving the way for more efficient and reliable 3DCP applications.

Arch structures primarily rely on compressive stress transfer, which helps alleviate the influence of anisotropy in 3D-printed (3DP) concrete; nevertheless, reinforcement incorporation in 3DP arches is still difficult to achieve. Integrating high-performance fibers for self-reinforcement at the material level is a pivotal strategy to enhance the ductility and reliability of 3DP structures. This study comparatively investigated the mechanical behavior of reinforcement-free 3DP arches fabricated with engineered cementitious composites (ECC) and polyoxymethylene (POM) fiberreinforced cementitious composites. Material tests showed that 3DP-ECC exhibited significant strain-hardening behavior with a tensile strain capacity of 5.18 %, whereas 3DP-POM presented tension-softening. These differences significantly affected the structural response. Under L/4 loading, both arches underwent asymmetric instability failure. The 3DP-POM arch failed in a brittle manner with a single macro-crack and a limit displacement of only 4.61 mm. In contrast, the 3DP-ECC arch demonstrated a ductile failure mode involving multiple interacting cracks, reaching a displacement of 13.6 mm, which yielded a ductility index 1.9 times that of the 3DPPOM arch. Finite element parametric analysis confirmed the superior ductility of 3DP-ECC arches compared to ultra-high performance concrete counterparts and identified the L/4 eccentric load as a more critical loading condition than mid-span loading. These findings provide essential insights for the design and construction of reinforcement-free 3DP concrete arches.

To overcome the strong mesh dependence and low computational efficiency of conventional phase-field fracture simulations based on finite element method (FEM), this study proposes a novel framework for efficient and accurate fracture analysis by integrating the Quadrature Element Method (QEM) into the phase-field formulation. In this formulation, the variational functional is discretized via numerical integration, and spatial derivatives are subsequently approximated using differential quadrature at integration points, enabling high-order approximation without explicitly constructing conventional FEM element shape functions for field interpolation; the differential-quadrature weighting coefficients are instead derived from Lagrange cardinal interpolation polynomials. This technology facilitates flexible high-order element construction and accurate representation of stress fields near crack tips. In addition, an alternate minimization algorithm is employed to solve the coupling between displacement and phase-field evolution while maintaining the crack irreversibility. The performance of the proposed method is demonstrated through a series of benchmark problems, including three-point bending, single-edge shear, asymmetric double-notched tension, and a 3D-printed Y-shaped cracking specimen. Numerical comparisons show approximately 95.6% fewer degrees of freedom in the single-edge shear specimen and up to 99.98% fewer elements in the asymmetric double-notched tensile specimen than in the cited FEM-based phase-field models, demonstrating substantial reductions in discretized model size. Moreover, the proposed method provides accurate near-tip stress predictions on coarse meshes, mitigates locking, and maintains stable numerical behavior for the mildly distorted mesh examined. Overall, the combination of high-order approximation and coarse-mesh adaptability makes the proposed phase-field QEM a promising solution for simulating complex crack evolution in engineering applications.

A novel methodology is introduced for simultaneously optimizing both the structural topology and printing paths in 3D concrete printing (3DCP). Drawing inspiration from the inherent geometric characteristics of 3DCP, this approach anchors its optimization framework within discrete frame structures. Beyond conventional frame topology optimization, we introduced the concept of simultaneous path optimization, bridging the gap between optimal design conception and its seamless realization in manufacturing. Leveraging the mechanical anisotropy inherent to 3DCP, we strategically orient the printing directions such that each frame member is printed along its longitudinal axis, optimizing for strength and material efficiency. To ensure direct printability, we impose constraints whereby the width of each member must be an integer multiple of the nozzle size, and the printing path must traverse continuously, covering the entire design without any intersections or overlaps. An Eulerian circuit, rooted in graph theory, fulfills part of these requirements, with additional constraints introduced to rigorously eliminate intersections and overlaps. The comprehensive simultaneous optimization problem is then formulated as a Mixed-Integer Linear Program (MILP), which is readily solvable. The application of this methodology to various test problems has yielded successful outcomes, demonstrating the efficacy and practicality of our approach in achieving simultaneous optimization of both structural topology and printing paths in 3DCP.

The internal micro-defects of 3D printed concrete (3DPC) play a pivotal role in influencing its mechanical properties. Nonetheless, the acquisition of representative internal micro-defect information is hindered by computational inefficiencies and quantification limitations of the current equipment system. This paper proposes a deep learning based system to assist SEM equipment in automatically quantifying micro-defects of 3DPC for indepth microstructural analysis that surpasses traditional SEM methods. Through optimal resizing approach and model enhancement tactics, the proposed micro-defect segmentation model leverages advantages of both convolutional neural networks and transformer. This improvement segmentation capability achieves higher accuracy and faster speed than current algorithms, enabling system to achieve accurate quantitative analyses of micro-defects. Using this automated analysis system, the relationship among micro-defect areas in 3DPC, mechanical properties, and printer parameters is investigated. Therefore, the proposed system reduces labour and computational time, demonstrating significant potential for applications in analyzing concrete microstructure.

A methodology for determining elastic engineering constants in an orthotropic constitutive model of hardened three-dimensional (3D)-printed concrete (3DPC) is introduced in this paper. The 3DPC material's unique configuration of filaments and layers weakens the mechanical interfaces between them, resulting in clear anisotropy along the three principal directions. To address this, a numerical model of a composite representative volume element (RVE) is created that combines a continuum concrete damaged plasticity model for the filaments with an interfacial cohesive zone model for the interfaces. The model's parameter values are determined through uniaxial compression, splitting tension, cross-bonded tension, and inclined shear tests. By exploring the model's design space using the design of experiments (DOE) technique, quantitative relationships between the orthotropic elastic engineering constants and the material properties of the filaments and interfaces are obtained, leading to explicit and simple formulas. Comparisons between numerical and experimental results for two hollow beams validate the effectiveness of the developed formulas.

This paper proposes a robust method utilizing Multi-state Fuzzy Bayesian Network (MFBN) to evaluate bridge safety risks, offering a foundation for risk control. Initially, a bridge collapse fault tree and a directed acyclic graph are constructed to analyze causal relationships. Subsequently, bridge nodes are categorized into three states, enhancing traditional binary states. Expert judgment ability and subjective reliability levels are considered to ensure survey data reliability, using confidence indices to establish multi-state fuzzy conditional probability tables. Lastly, the improved similarity aggregation method incorporates age as a factor to aggregate expert opinions, determining safety risk probabilities of root nodes. This approach assesses bridge safety risk levels with prior knowledge and evidence, identifying critical nodes contributing to heightened risks, aiding in the formulation of risk management strategies. Application to two urban bridges demonstrates the method’s effectiveness and robustness, positioning it as a valuable decision-making tool for bridge safety risk management.

To overcome the shortcomings of the Arithmetic Optimization Algorithm (AOA) in solution accuracy and convergence speed, this paper proposes an improved approach based on reinforcement Q-learning and Random Elite Pool strategy (QL-REP-AOA). The algorithm constructs a state space based on the iteration process and designs a nonlinear reward function with stage adaptability. With this design, the algorithm can dynamically select the optimal search strategy based on the characteristics of each stage of the optimization problem. Additionally, the Random Elite Pool strategy is introduced, which enhances population diversity and search efficiency through the collaborative effect of multiple search operators. To validate the effectiveness of the proposed algorithm, experiments are conducted on 27 classical benchmark functions, the CEC2020 test set, and real-world engineering problems. The experimental results show that QL-REP-AOA outperforms other optimization algorithms in both accuracy and convergence speed, demonstrating its potential in solving complex optimization problems.

Extrusion-filament and no-framework craft significantly influence microcracks in 3D printing concrete (3DPC). A detailed analysis of these microcracks is essential to improve overall performance of material. However, fast and automated methods for capturing and measuring representative microcrack information in 3DPC are currently lacking. This paper presents a transformer based method for automatic quantization of microcosmic information in 3DPC, enabling a comprehensive analysis of microcracks. Additionally, a transformer network to rapidly and cost-effectively obtain high-quality microscopic images is introduced. The proposed quantization method involves a range of enhancement tactics over an existing baseline model, demonstrating higher accuracy in detecting inner microcracks of 3DPC compared to current advanced algorithms. This method surpasses existing microscopic imaging technologies in terms of information content, computational speed, and cost-efficiency. Therefore, this method will have promising applications for analyzing other micro-details in concrete when it is supplemented with a diverse and extensive training dataset.

This study systematically investigates the bending performance of 3D printed concrete (3DPC) grid components, focusing on the synergistic effects of printing patterns and interfacial anisotropy. Experimental and numerical analyses were conducted on W- and Y-grid specimens fabricated with 10 mm and 20 mm nozzles. A refined finite element model (RFEM) integrating plastic damage elements for filaments and bilinear cohesive zone models (CZM) for interfaces was developed. Key findings reveal that Y-grid components exhibit 80 % higher load-bearing capacity than W-grid counterparts, attributed to enhanced node strength, uniform stress distribution, and increased moment of inertia. Smaller nozzles improve mechanical performance by reducing interfacial defects, with 10 mm nozzles increasing ultimate loads by 10-24 % compared to 20 mm nozzles. Crucially, interfacial damage remained absent prior to structural failure, demonstrating that optimized printing patterns can mitigate anisotropy effects. The validated RFEM achieved less than 10 % error in predicting failure loads and displacements, confirming its utility for 3DPC design. This work advances the understanding of pattern-driven structural optimization in additive manufacturing and provides a computational framework for performance prediction.

Early-age cracking remains a major durability challenge for concrete. It is primarily caused by internal restraint stresses induced by humidity and temperature gradients during hydration. Conventional approaches often fail to capture the coupled and non-uniform nature of heat and moisture transport, limiting their ability to predict cracking risk and evaluate mitigation strategies. To address this limitation, we characterize the spatiotemporal evolution of internal humidity and temperature using a spatial coefficient of variation. From a numerical standpoint, the influence of polypropylene fibers (PPFs) on internal relative humidity is elucidated by adopting an unconditionally stable backward-Euler finite-difference scheme to resolve multiple coupled physicochemical processes—hydration, heat release, self-desiccation, heat and moisture diffusion to the environment—and their mutual interactions. Furthermore, a one-dimensional homogeneous random-field model is proposed to quantify the spatial non-uniformity of humidity in PPF concrete. On this basis, the effects of polypropylene fibers (PPFs) in mitigating internal humidity is quantitatively revealed. Good agreement is achieved between simulations and tests, with standard deviations of 0.0119 for normal concrete and 0.0041 for PPF concrete, thereby validating the model’s predictive capability for the spatiotemporal distribution of internal relative humidity (RH) in PPF concrete. According to the numerical analysis, owing to the moisture-sorption characteristics of PPFs, at a depth of 25 mm, the internal RH in PPF concrete has decreased by 16% at 28 days, whereas normal concrete exhibits a 28% decrease. With increasing depth, the RH reduction at 28 days is approximately 13% for both PPF concrete and plain concrete, and the time-dependent evolution of RH in PPF concrete is broadly similar to that of normal concrete. Furthermore, the mitigating influence of PPFs decreases with hydration age and distance from the surface, reflecting the gradual decline of diffusion heterogeneity over time and depth. These findings provide new numerical evidence for the effectiveness of PPFs in reducing the early-age cracking risk in concrete.

Electromagnetic wave (EMW) pollution poses a tremendous effect on information security and human health. However, ordinary concrete structure incorporated by ferrite fiber lacks EMW absorption flexibility to form electromagnetic superstructures. 3D printing technology paves an effective way to facilitate the anisotropy of electromagnetic absorption capacity by generating directional effects on ferrite fiber. This research evaluates the influence of 3D-printed fiber-oriented superstructure on EMW absorption performance with an equivalent waveguide attenuator model. The microwave-absorbing cementitious composite (10% magnetite and 25% copper slag) was prepared to incorporate 0.5 wt% copper fibers (CF) and steel fibers (SF), respectively. Absorption elements in each group are prepared by laminar parallel printing, cross-printing, and zigzag printing. In addition, the EMW absorption capability (ranging from 2 GHz to 18 GHz) was investigated by the Naval Research Laboratory (NRL) equipment. The overall EMW absorption performance of the SF samples is superior to CF samples. The optimized order of the EMW absorption performance of CF-reinforced samples is determined as zigzag, parallel, and cross printing, while SF is parallel, cross, and zigzag printing methodology. Overall, the laminar parallel printed steel fiber element gave the best shot with a peak reflectivity of - 16.34 dB and an absorption bandwidth of 13.15 GHz. Meanwhile, SF-reinforced specimens all demonstrated absorption peaks around 8 GHz, while CF-incorporated samples' absorption peaks appeared at both 8 GHz and 12 GHz, offering multiple design and application choices according to engineering requirements. Finally, an equivalent attenuator model is suggested for illustrating the superimposed reinforcement, spatial impedance matching, and multiple scattering of dielectric properties.

In this study, four specimens with two cross-section patterns, the W-shape and Y-shape, are printed by using two nozzle sizes to study the effects of the printed patterns and nozzle size on the mechanics properties of 3D Printed Concrete (3DPC) structures. The four-point loading tests are conducted and the Digital Image Correlation (DIC) analysis technology is used. The results showed that the load capacity of the Y-shaped specimens is about 10

It is well recognized that layer and filament interfaces are the mechanically weakest locations in 3D printed concrete (3DPC) structures. Therefore, interfacial cracking between the layers and filaments is investigated experimentally and numerically. First, direct tensile and inclined shear tests are carried out to measure the tensile and shear load-displacement relationships. Displacement and strain are recorded using the digital image correlation (DIC) technique. Parameter values required in a cohesive zone model (CZM) for simulating the interfacial cracking are extracted from the experimental data. Then a finite element model incorporating the CZM is established to predict the interfacial cracking. The effectiveness of the numerical model is verified by comparing the simulation results with the experimental data.

The relationship between crack comprehensive characteristics and the stiffness of RC beams is investigated through experimental tests and numerical simulations. A synthetic parameter, the surface damage ratio (SDR), is proposed to represent the comprehensive characteristics of cracks, including crack width, length and location. Crack propagation in RC beams is simulated using a dual-damage parameter plastic damage model. A method for calculating the width and length of cracks is developed based on integral point strains and element equivalent length. The accuracy of this method is verified by comparing it with the results of four-point bending tests on RC T-beams and rectangular beams. Correlation analysis indicates that the cracks in different zones have varying impacts on beam stiffness, with crack length in the bending area and crack width in the shear area having a greater impact than others. To consider the comprehensive characteristics of cracks and their impact on beam stiffness, the SDR, the ratio between the entire crack area and the intact area of the beam surface, is proposed. The proposed parameter is shown to increase linearly with load, resulting in beam stiffness degradation following a power law. The accuracy and applicability of the proposed SDR and its correlation with load and stiffness are verified through load tests on rectangular beams with different reinforcement ratios using Cervenka's test. Parameter analysis has shown that the section modulus, concrete strength, reinforcement arrangement, and ratios significantly influence how beam stiffness varies with SDR. The presented simulation model for concrete crack evolution, combined with the synthetic parameter of crack characteristics, and the correlation analysis, provide a valuable approach for the safety evaluation of concrete beams during their service life.

A comprehensive study on the buckling mechanism of the Cold-Formed Steel (CFS) box beam composed of C- and U-shaped channels is conducted in order to improve the accuracy of the Direct Strength Method for calculating the bending capacity of CFS beams. Experimental tests involving four-point bending are carried out on six CFS box beams with varying screw spacing and stiffener numbers. In addition, numerical analyses are performed by finite element method. Based on the investigation of experiments and parameter analysis, an elastic constraint model for web buckling is proposed and the buckling coefficient are derived by the application of a generalized variational principle. The experiments reveal an interaction between local buckling in the upper flange under compression and the web. When the upper flange in compression buckles before the web, it leads to a reduction in the lateral constraint stiffness of the flange on the web, consequently, resulting in web buckling. An elastic constraint model for web buckling is proposed, incorporating the constraint effects of the upper flange's elastic support on the web. The critical buckling stress and buckling coefficient of the model are derived using the generalized variational principle. According to the experimental and numerical analysis results, the relative restraint stiffness of the flange on the web shows effects on the buckling coefficient. As the screw spacing and the length of the pure bending area decrease, as well as the number of stiffeners increases, the relative restraint stiffness increases. Subsequently, this leads to an increase in the buckling load and ultimate displacement of the CFS beam. Considering the interaction between the buckling of flange and web, the Direct Strength Method in AISI-2016 is modified for the CFS built-up box beam composed of C- and U-shaped channels. The applicability and correctness of the proposed modified Direct Strength Method is approved through the application of multi-box CFS beams composed of 2C-2 U and 3C–2 U channels. Compared with the Effective Width Method in current Chinese code and Direct Strength Method in AISI-2016, the proposed modified DSM exhibits the highest accuracy in predicting the bending capacity of the CFS box beam. It is shown that the proposed method can be effectively employed to calculate the flexural capacity of CFS beams with multi-box under local buckling control.

Insulator defects in transmission lines can threaten the safe and stable operation of the power system. Therefore, insulator defect detection plays a crucial role in the electricity sector. Currently, the you only look once (YOLO) algorithm is widely used in insulator fault detection. However, insulator defect detection using YOLO still faces the double challenges of detection precision and real-time performance. Therefore, we construct a lightweight model for insulator defect detection named coordinate attention mechanism (CAM) and feature channel shuffle operation (CSO) YOLO (CACS-YOLO). By introducing the CAM and CSO in the original YOLOv8m, we improve the detection precision and reduce the parameters of the model, compared with existing insulator defect detection models. Meanwhile, in order to solve the problem of insufficient data and cope with different weather conditions, we use synthetic weather algorithms for data enhancement and construct a synthetic foggy and rainy insulator dataset (SFRID). The experimental results show that the mean average precision (mAP) of CACS-YOLO is 96.9%, and the parameters of the proposed model are 27.44 M. These outcomes show the high-precision and lightweight of our proposed model in insulator defect detection. The source code and dataset are available on GitHub.

The substitution of cement, natural sand, and synthetic fibers in concrete with solid wastes substantially reduces CO2 emissions. Incorporating solid wastes into 3D printed concrete, which reduces CO2 emissions from labor and formwork, can achieve innovative low-carbon construction materials. However, the previous studies lack a comprehensive review on the inherent characteristics of various solid wastes and their impacts on the printability, mechanical performance, and functionality of 3D printed concrete. This paper reviews the present state of research and introduces feasible methods for the sustainable use of solid wastes in 3D printed concrete. Numerous types of solid wastes are investigated comprising their benefits, shortages, and fit dose to guide in selecting the optimal solid waste to enhance the performance of printed concrete. Additionally, the printability, functionality, and mechanical strength of printed concrete with solid waste addition are quantified and summarized. The preliminary exploration to achieve a balance between multi-functionality and mechanical properties using mixed solid wastes is conducted for 3D concrete printing. Finally, machine learning, broadening raw material sources, automated monitoring systems, multi-functionalized composite, and BIM are proposed to address current challenges.

In the manufacturing process of 3D Concrete Printing (3DCP), defects and anomalies have a significant impact on both the success rate and the quality of the final products, underscoring the need for real-time monitoring. Currently, monitoring is primarily based on manual observation and existing automated methods are limited in real-time performance and accuracy. This study introduced a real-time and highly accurate defect detection and measurement system for using deep learning (DL) and computer vision (CV) techniques. A range of improvement methods were applied in YOLOv7, showing better capacities of accuracy and speed for detecting defects in 3DCP than current cutting-edge detectors such as YOLOv8. Notably, the virtual high-fidelity data were produced by DL based data augmentation strategy and their effects were assessed. Replacing real data as the training dataset, the generated virtual data were used in the models to improve measurement accuracy. Applying the proposed method, the comprehensive insights into 3DCP defects were obtained. Consequently, the relationship formula between defect frequency and printer parameters was investigated by the proposed method, guiding operators in effectively controlling printer parameters and preventing breakpoint defects during the printing process.