Ultra-high-performance geopolymer concrete (UHPGC) offers a promising low-carbon alternative to Portland-cement-based ultra-high-performance concrete, yet its mechanical behaviour is governed by a highly non-linear interplay among mix proportions, activator chemistry and curing age. To support data-driven mix-design optimisation, we curated and released a benchmark dataset containing 427 distinct UHPGC mixes with compressive-strength records at eight curing ages (3-360 days). Leveraging on this, we propose a hybrid deep-learning framework that cascades one-dimensional convolutional neural networks (CNNs) and long short-term memory (LSTM) layers with a Feature Masking Attention (FMA) module. The CNN extracts local compositional patterns, the LSTM captures latent temporal dependencies across ages, and FMA randomly masks input features during training while learning adaptive attention weights, thereby enhancing generalisation and interpretability. Five-fold cross-validation demonstrates that the CNN-LSTM-FMA model achieves an R2 of 0.904 +/- 0.018, an RMSE of 5.56 MPa and an MAE of 3.38 MPa, surpassing conventional deep-learning baselines and ablated variants by 4-16%. Moreover, an analysis of the model's attention mechanism, corroborated by SHAP importance analysis, reveals that cement (Cem), coarse aggregate (Cag), and ground-granulated blast-furnace slag (GGBS) are the most influential factors governing strength development. The proposed dataset and model constitute an open, extensible platform for AI-assisted, low-carbon concrete design.

A novel precast concrete (PC) shear wall system connected to steel columns with friction bearing devices (FBDs) was recently proposed and experimentally validated, owning excellent ductility and sufficient energy dissipation capacity. However, almost no researches have been conducted to model the behaviors of the novel PC shear wall system. This paper presents a simulation technique developed with OpenSees models and validated against experimental results. The numerical model demonstrates good accuracy in predicting load-bearing capacity, cumulative energy dissipation, and FBD behavior, with a maximum error in peak load of no more than 7.9

One of the most common pathologies on masonry building facades is mortar loss, which can lead to insufficient bonding force, uneven stress distribution between blocks and reduced structural bearing capacity. Traditional manual visual detection is a laborious, inefficient and subjective process often conducted through sampling inspection, which cannot fully reflect the overall quality of masonry. In contrast, automatic detection based on deep learning can significantly enhance accuracy and efficiency. Therefore, the goal of this paper is to validate the feasibility of applying deep learning methods for the automatic detection of mortar loss in images of masonry facades. To achieve this, datasets for classification and segmentation were created with complex backgrounds and varying degrees of mortar loss, based on field investigations and smartphone photography results. Several convolutional neural networks were compared, revealing MobileNetV3 as the best-performing model at the patch level, achieving an accuracy of 98.68

Spalling is a prevalent form of damage in masonry structures that significantly affects their structural safety, durability, and load-bearing capacity. Currently, the inspection of masonry structures relies on manual methods, which poses challenges in ensuring timely and accurate evaluations of structural damage. To address these concerns, this study develops a pixel-level segmentation model called Segmentation for Masonry Spalling Net (SMS-Net) based on YOLOv8-seg. Additionally, a dataset comprising 3600 images for pixel-level masonry spalling segmentation was established. Given the typical forms of masonry spalling damage are complex irregular polygons, this study introduces three specific optimization strategies: substitute the standard convolution module with the dynamic snake convolution module; incorporate the focused linear attention module after the spatial pyramid pooling layer; replace the Complete Intersection over Union (CIoU) bounding box loss with the Minimum Point Distance Intersection over Union (MPDIoU) bounding box loss. Based on the experimental results, all three optimizations notably enhance the model's capability to segment masonry spalling compared to YOLOv8-seg, achieving a Mean Intersection over Union (MIoU) of 77.8 %, which represents a 7 % improvement over BaselineYOLOv8. Additionally, compared to other state-of-the-art segmentation models, SMS-Net also demonstrates superior segmentation performance. The proposed model provides an efficient approach for the automated inspection of spalling in masonry structures, thus aiding in the quick assessment and reinforcement of masonry structures.

Longitudinal elongation due to reversed cyclic inelastic deformation is common in Reinforced Concrete (RC) shear walls, beams, and column members. In contrast to conventional RC shear walls that have only one stiffness transition opportunity associated with material hardening, a precast shear wall equipped with Friction Bearing Devices (FBDs) positioned at the vertical connections to bounding end columns and taking advantage of longitudinal elongation can generate additional hardening stages. This assemblage is referred to as a Multiple Hardening Precast Concrete shear Wall (MHPCW). The multiple hardening mechanisms are explained using Modified Compression Field Theory based on pseudo-static reversed cyclic loading tests, and a displacementbased analytical model is developed to predict the response in terms of lateral load, lateral displacement, and longitudinal elongation through an iterative approach. This model offers a means to decouple the lateral and longitudinal responses and gain insights into the axial constraint. Finally, the validity of the model was experimentally validated.

Weatherboards, a unique feature of historical timber bridge heritages, create enclosed spaces affecting the fire dynamics. However, the influence of weatherboard height on the fire dynamic characteristics of timber bridge heritages remains poorly understood, and the relationship between weatherboard height and fire behavior has rarely been investigated. In this study, aged wood specimens were collected from a timber bridge and tested using cone calorimetry to establish a pyrolysis model for deteriorated wood. Subsequently, the fire development process in timber bridge heritages with five different weatherboard heights (no cover, 25 % cover, 50 % cover, 75 % cover and fully cover) was simulated using the Fire Dynamics Simulator (FDS). Parameters such as the total heat release rate (HRR), gas temperature, smoke layer height and internal gas flow were measured and analyzed. A novel theoretical model for smoke layer height in historical timber bridge heritages was proposed, which agreed well with the FDS results. Finally, statistical analysis of fire parameters was performed. Range analysis was employed to evaluate the sensitivity of different fire parameters to weatherboard height, while the entropy method was utilized to calculate the fire risk score (FRS) for the historical timber bridges with varying weatherboard heights. The experimental and simulation results, along with the proposed smoke layer thickness calculation model outputs, provided a reference for fire protection and fireproofing design of historical timber bridge heritages with different heights of weatherboards.

Steel-concrete composite bolted connections (SCCBCs) are frequently used for the horizontal connections of precast concrete (PC) walls, which makes the walls resilient and easy to replace after earthquakes. In a newly developed PC wall named as the multiple hardening PC shear wall (MHPCW), the SCCBCs are extended to vertical connections and are characterized by limited travel behavior, referred to as friction bearing devices (FBDs). The FBD plays a crucial role in the multiple hardening behavior by leveraging the longitudinal elongation, and friction and limited travel are identified as key factors in design. Although previous experimental results have demonstrated the promising seismic performance of the MHPCW, a deeper understanding of the multiple hardening mechanism is still needed. This study introduces a macro element modeling approach for the MHPCW. The validation of various failure modes and both lateral and longitudinal responses is conducted through experimental results. A parametric analysis of the MHPCW is conducted to explore the influence of key parameters, including the limited travel of the FBD, FBD friction, stiffness of end column, and axial load ratio. Furthermore, design recommendations for the FBD are proposed and validated by both experimental and numerical results, with failure modes fully considered, thus advancing the understanding of MHPCW.

With advancements in renewable energy and the swift expansion of the electric vehicle sector, lithium-ion capacitors (LICs) are recognized as energy storage devices that merge the high power density of supercapacitors with the high energy density of lithium-ion batteries, offering broad application potential across various fields. This paper initially presents an overview of the developmental history, energy storage mechanisms, and classifications of LICs. It then concentrates on the latest advancements in anode and cathode materials for LICs, systematically reviewing strategies for optimizing electrochemical performance through microstructure adjustment, elemental doping, and the use of composite materials. Furthermore, it delves into the recent progress in the electrolyte system of LICs and prelithiation technologies, examining the features of different electrolyte systems and detailing various prelithiation approaches along with their merits and drawbacks. In conclusion, this paper summarizes and anticipates the current research trends in LICs, offering new perspectives and directions for future investigations.

Accurate 3D reconstruction and dimension recovery of engineering structures are crucial for dimensional inspection, whereas classic SfM + MVS reconstruction only generates scaled similar models. To address this issue, a 3D reconstruction and dimension recovery algorithm using sequential images and photography trajectories is proposed. The algorithm improves traditional 3D reconstruction by integrating inertial measurement unit (IMU) data to estimate photography trajectories, down-sampling and time-aligning trajectory points using timestamps of sequential images as reference, and solving the scale factor via data fusion to recover absolute dimensions. Laboratory validation on specimens shows that the proposed algorithm achieves a 0.58 % mean relative error (MRE) between calculated and measured dimensions, confirming high accuracy; further validation on practical structures demonstrates that, paired with the smart terminal-based mobile scheme, the proposed algorithm yields 1.29 % MRE for small-scale components and 2.73 % for large-scale structures, further verifying the engineering practicability.

Zinc-ion batteries (ZIBs) offer safe, low-cost, high-capacity energy storage, but dendrite growth, hydrogen evolution, and corrosion limit their use. This paper reviews stability strategies and research directions.

To fully utilize Chinese fast-growing timber resources, fast-growing poplar was selected for manufacturing flame-retardant laminated veneer lumber (FRLVL). Firstly, orthogonal experiments were conducted to assess the impact of four factors (hot-pressing time, hot-pressing temperature, retardant concentration, and retardant types) on the mechanical properties and burning behavior of FRLVL. Subsequently, optimal manufacturing parameters were chosen based on statistical analysis. Finally, the fire performance of LVL manufactured with the optimal parameters was evaluated to investigate changes in physical–mechanical properties under high-temperature conditions. Results indicated that the addition of retardants led to a decrease in mechanical properties. In comparison to the control group, LVL impregnated with two retardants exhibited higher limited oxygen index and longer fireproof time, with the effects of ammonium polyphosphate (APP) surpassing those of borax (BX). The optimal manufacturing parameters were a hot-pressing temperature of 140 °C, a hot-pressing time of 1.3 min/mm, and concentrations of 15

Wastes plastics have caused serious environmental pollution due to their difficult degradation, therefore, there is an imperious demand for their recycling. It is known that waste polyethylene terephthalate (PET) can be degraded into sodium terephthalate (Na2TP) and ethylene glycol (EG) in alkaline aqueous solution, which could be utilized as the raw material for the manufacture of new PET. However, there are some obstacles in the recycling of waste plastics, such as low degradation efficiency, and complicated separation. Here, we report a novel method to degrade waste PET completely at 200 celcius in 6 h, based on less solvent solid-state reaction (LSR) of PET and NaOH in the presence of a little amount of water and equivalent EG to terephthalate, the obtained product is sodium terephthalate (Na2TP) and EG. The kinetic of waste PET degradation fits to D2 or D1 model. Furthermore, into the reaction mixture is a stoichiometric concentrated H2SO4 added to obtain terephthalic acid (TPA) at room temperature. This work provides a strategy with practical application value for efficient and selective degradation of plastics, easy product separation, and recycling of raw materials, and also affords a promising scheme for the recycling economy of other ester-based waste polymers.

At present, numerous experiments have been conducted on precast shear walls with energy dissipation devices, which have proved the effectiveness of this approach. Previous experiments have revealed significant differences in hysteresis rules, skeleton curves, etc. between this type of wall and traditional shear walls. To provide the modeling approach of the precast shear wall with end columns containing friction-bearing energy dissipation devices (Friction-bearing shear wall) in terms of seismic behavior, a finite element model is developed and calibrated using the previous experimental results in this study. A thorough parametric analysis is conducted on the friction-bearing shear walls to establish a comprehensive database. Based on previous test results, an analytical model is proposed for the seismic design and assessment of friction-bearing shear walls. The reliability of the proposed analytical model is evaluated by comparing it with the database developed in this study as well as test results from existing literature. The results indicate that the proposed analytical model reasonably simulates the skeleton curve and cyclic response of the wall, especially the friction-bearing behavior, the unloading/reloading branch of cyclic load-displacement relationship, etc. Also, the numerical model is in good agreement with experiment results in crack development, failure mode, stress distribution, etc.

The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the “diameter calculation region” and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems.

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In recent years, a variety of nanomaterial synthesis methods have been used to explore the preparation of metal–organic frameworks (MOFs). The zeolitic imidazolate frameworks (ZIFs), are typically synthesized through high cost and environmentally unfriendly solvothermal methods, require a large amount of organic solvents, and have extremely low yields. Therefore, exploring scalable and environmentally friendly synthesis routes for ZIFs is crucial. The metal oxide and 2-methylimidazole are completely converted into submicron size ZIFs in stoichiometric ratio via less solvent solid state chemical reaction (LSR) method. The ZIFs synthesized by LSR method have good crystallinity, large specific surface area, and the use of tiny solvent amount can achieve a yield of nearly 100%, which solves the problems of low yield and serious waste of solvent in the synthesis of ZIFs by traditional methods. Additionally, the fabricated ZIFs derived materials through high-temperature pyrolysis have high specific surface area, nitrogen rich content, and excellent chemical stability, being favorable for promoting the adsorption of organic dyes. The adsorption capacity of ZIF-8 derived materials fabricated by the LSR method for organic dyes is significantly better than that of ZIF-67 and bimetallic ZIFs (BZIFs) derived materials. Compared with the traditional method, the LSR method is a green and scalable manner to synthesize submicron ZIFs materials, paving the way for the preparation of various ZIFs with different metal centers, and also providing a new strategy for the commercial production of ZIFs derived materials widely used in the environment protection.

Supercapacitors (SCs) are a novel type of energy storage device that exhibit features such as a short charging time, a long service life, excellent temperature characteristics, energy saving, and environmental protection. The capacitance of SCs depends on the electrode materials. Currently, carbon-based materials, transition metal oxides/hydroxides, and conductive polymers are widely used as electrode materials. However, the low specific capacitance of carbon-based materials, high cost of transition metal oxides/hydroxides, and poor cycling performance of conductive polymers as electrodes limit their applications. Copper–sulfur compounds used as electrode materials exhibit excellent electrical conductivity, a wide voltage range, high specific capacitance, diverse structures, and abundant copper reserves, and have been widely studied in catalysis, sensors, supercapacitors, solar cells, and other fields. This review summarizes the application of copper–sulfur compounds in SCs, details the research directions and development strategies of copper–sulfur compounds in SCs, and analyses and summarizes the research hotspots and outlook, so as to provide a reference and guidance for the use of copper–sulfur compounds.

Lead formate (LF) has been successfully prepared from compounds in spent lead-acid batteries by a simple and low-cost method. The irregular sheets of LF pile up to form agglomerated particles. When it is used as an additive in the negative electrode, it makes the electrode perform better and be able to discharge a capacity of 107 mAh g −1 at 100 mA g −1 to 1.75 V; it still discharges 86.5 mAh g −1 after 1200 cycles. EIS shows that the electrode has lower resistance and higher ion diffusion rates than the one without LF. The reason could be that LF produces formic acid when it meets sulfuric acid, and the formic acid could clear up the oxide on lead alloy grid as well as basic lead sulfate in the electrode, thus making the conduction network grow better.

An innovative bolt-connected precast concrete (PC) shear wall structural system has recently been proposed for middle and high-rise buildings in seismic regions. As a further research step, this paper aims to investigate the seismic behavior of PC coupled shear wall with different types of dissipative coupling beams. The quasi-static cyclic tests on two PC coupled shear wall specimens with friction-based coupling beam (FCB) and yielding-based coupling beam (YCB) were conducted. Moreover, nonlinear finite element models of the specimens were established and validated with the experimental results. The results demonstrated that the proposed dissipative coupling beams can effectively couple the wall panels in elastic field, whilst they can sustain large plastic deformation adding to the structural assembly a relevant source of energy dissipation under thresholded actions. Whilst the specimen with YCB exhibited large overstrength in plastic field, the specimen with FCB provided a more stable plateau capacity associated to enhanced deformation capacity, ductility and energy dissipation. Moreover, the FCB resulted practically undamaged and immediately reusable at the end of the test, whilst the YCB was found highly plasticized and locally torn. In addition, the detailed finite element models of the test specimens accurately predicted the experimental behavior.