Developments in the Built Environment最新文献_第3页

Assessment of the pozzolanic reactivity of aggregate washing fines for use in Cement-Based Materials 水泥基材料用骨料洗涤细粉的火山灰反应性评价

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-16 DOI: 10.1016/j.dibe.2026.100856

Abiza Yaakoub, Saleh Hayat, Ferroukhi Mohammed Yacine, Mesticou Zyed, Kacem Mariem, Si Larbi Amir

This study focuses on the valorization of fines from aggregate washing (AWF), an industrial residue with a high clay mineral content, as a pozzolanic alternative to additions in cementitious binders. The Fines, were subjected to thermal activation at 750 °C to induce a change in their crystalline structure towards reactive amorphous phases. Chemical and mineralogical analyses revealed a composition rich in silica, alumina and clay minerals such as smectite, illite and kaolinite, indicating strong pozzolanic potential. The suitability of this heat treatment was supported by thermogravimetric criteria, including a loss on ignition of 9.38 % and a dehydroxylation peak observed at 477 °C. Following calcination, the samples showed a reduction in specific surface area and changes in particle size, reflecting the effects of internal thermal transformations. Pozzolanic activity was evaluated through mechanical tests in compliance with NF EN 450-1 and EN 196-1 standards revealing encouraging results, particularly for cement substitution rates of between 10 % and 20 %. Compressive strength measurements and thermogravimetric analysis confirmed effective portlandite consumption in formulations containing calcined, confirming their reactivity. Finally, their incorporation into concrete formulations using the Dreux-Gorisse method has enabled a partial reduction in cement without significantly altering mechanical performance, thus supporting a sustainable approach combining waste recycling and a reduction in concrete's carbon footprint.

本研究的重点是集料洗涤（AWF）细粉的增值，这是一种具有高粘土矿物含量的工业残留物，作为胶凝粘合剂中添加物的火山灰替代品。在750 °C下进行热活化，诱导其晶体结构向反应性非晶相转变。化学和矿物学分析显示，其成分中含有丰富的二氧化硅、氧化铝和粘土矿物，如蒙脱石、伊利石和高岭石，表明其具有很强的火山灰潜力。热重标准支持了这种热处理的适用性，包括9.38 %的着火损失和在477 °C时观察到的去羟基化峰。煅烧后，样品的比表面积减小，颗粒大小发生变化，反映了内部热转化的影响。通过符合NF EN 450-1和EN 196-1标准的机械测试来评估火山灰活性，结果令人鼓舞，特别是水泥替代率在10 %至20 %之间。抗压强度测量和热重分析证实了含有煅烧的配方中有效的硅酸盐消耗，证实了它们的反应性。最后，使用Dreux-Gorisse方法将其加入混凝土配方中，可以在不显著改变机械性能的情况下减少水泥的部分用量，从而支持将废物回收和减少混凝土碳足迹相结合的可持续方法。

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引用次数: 0

Effect of wind-waves misalignment on the dynamics and power performance of floating offshore wind turbines 风浪失调对浮式海上风力机动力性能的影响

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-16 DOI: 10.1016/j.dibe.2026.100853

Muhammad Naeem , Xuhui He , Haiquan Jing , Shahzad Muhammad Ali , Zahid Ullah , Shiqin Zeng

Floating offshore wind turbines (FOWTs) are crucial for accessing deep-water wind resources, yet their operation is complicated by frequent wind–wave misalignments that affect stability and power performance. These misalignments stem from differing wind and wave generation mechanisms, including rapid wind-direction shifts and distant swells. This study evaluates the dynamic structural response and power performance of an FOWT under misalignment angles from 0° to 120° in increments of 30°. Results show that misalignment amplifies lateral and rotational platform motions, with sway and roll dominating at β = 90°–120°, while surge decreases with increasing misalignment. Tower-base pitch and yaw moments peak at β = 90°, indicating elevated fatigue risk. Power output decreases by up to 57 % under extreme misalignment, with fluctuations stabilizing at higher β due to reduced aerodynamic efficiency. These outcomes emphasize the need to account for wind–wave misalignment in FOWT design, control strategies, and site-assessment practices.

浮式海上风力涡轮机（FOWTs）对于获取深水风力资源至关重要，但由于频繁的风浪失调而影响稳定性和功率性能，其运行变得复杂。这些失调源于不同的风和波的产生机制，包括快速的风向变化和遥远的涌浪。本研究评估了在0°到120°的偏差角度（增量为30°）下，FOWT的动态结构响应和功率性能。结果表明：在β = 90°~ 120°范围内，平台横向和旋转运动以摇摆和横摇为主；塔基俯仰和偏航力矩在β = 90°处达到峰值，表明疲劳风险升高。在极端不对准下，功率输出减少高达57 %，由于空气动力效率降低，波动稳定在更高的β。这些结果强调了在FOWT设计、控制策略和现场评估实践中考虑风浪失调的必要性。

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引用次数: 0

Study of energy harvesting from conductive cement nanocomposites using a triboelectric nanogenerator 利用摩擦电纳米发电机收集导电水泥纳米复合材料能量的研究

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-15 DOI: 10.1016/j.dibe.2026.100854

Young-Eun Lee , Donghan Lee , Jihye Sung , Ilhwan You , Dongwhi Choi , Seung-Jung Lee

This study presents the systematic optimization and validation of a triboelectric nanogenerator (TENG) designed for practical application by addressing two key challenges: balancing mechanical strength with electrical conductivity and establishing system-level validation. Carbon black (CB), carbon nanotubes (CNTs), and carbon fibers (CFs) were incorporated at varying contents to determine the optimal composition. Incorporation of 0.5 vol% CNT yielded the optimal performance, achieving stable conductivity without compromising mechanical strength. Based on this optimized cement-based composite (CBC), a TENG system was fabricated consisting of a CBC electrode, a polydimethylsiloxane (PDMS) contact layer, and a nylon counter layer, which generated the highest average peak voltage of 22.4 V. Output performance was evaluated under different loads, excitation frequencies, and contact areas, with the device delivering a peak power of 3.364 μW at an optimal load resistance of 40 MΩ. Practical feasibility was demonstrated by powering a low-power electronic device. These findings highlight an optimized CBC-TENG design that integrates structural integrity with efficient energy harvesting, advancing the readiness of cement-based self-powered systems and offering a viable pathway for its integration into sustainable civil infrastructure.

本研究通过解决两个关键挑战：平衡机械强度和导电性以及建立系统级验证，提出了为实际应用而设计的摩擦电纳米发电机（TENG）的系统优化和验证。以不同的含量加入炭黑（CB）、碳纳米管（CNTs）和碳纤维（cf）以确定最佳组成。0.5 体积%碳纳米管的掺入产生了最佳性能，在不影响机械强度的情况下实现了稳定的导电性。基于该优化的水泥基复合材料（CBC），制备了由CBC电极、聚二甲基硅氧烷（PDMS）接触层和尼龙counter层组成的TENG系统，该系统产生的最高平均峰值电压为22.4 V。在不同负载、激励频率和接触面积下，器件输出功率峰值为3.364 μW ，最优负载电阻为40 MΩ。通过为低功耗电子器件供电，证明了其实际可行性。这些发现突出了优化的CBC-TENG设计，该设计将结构完整性与高效的能量收集相结合，提高了水泥自供电系统的准备程度，并为其集成到可持续民用基础设施中提供了可行的途径。

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引用次数: 0

Experimental study on human visual response to safety signage under emergency lighting conditions 应急照明条件下人眼对安全标识视觉反应的实验研究

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-14 DOI: 10.1016/j.dibe.2026.100852

Ke Wu , Haihang Hu , Huakai Sun , Kai Zhu , Xingfu Yu , Ye Jin , Tianhang Zhang

Reliable recognition of evacuation signage under low-visibility conditions is vital for occupant safety. This study investigates the impact of chroma differences (ΔC∗) on visual recognition and introduces a perception-based model tailored for supra-threshold tasks. Through psychophysical testing, recognition performance was quantified using Color Visual Acuity (CVA) across varying brightness, chroma, and hue conditions. Results reveal that CVA decreases with increasing chroma due to perceptual saturation and varies significantly with hue, particularly reduced near yellow (90°) due to S-cone sensitivity limitations. Brightness (L∗) consistently enhances CVA across all conditions. A novel Perceived Color Difference (PCD) model was developed, based on spectral radiance differences weighted by human chromatic sensitivity. The model exhibits a robust logarithmic correlation with CVA, outperforming traditional ΔE metrics, which are optimized for near-threshold color discrimination rather than recognition. A dual-threshold criterion, CVA ≥4.0 and PCD ≥0.0005, is recommended to ensure effective recognition in safety-critical environments. The findings support the design of more effective evacuation signage by linking human visual responses to lighting conditions in low-visibility environments.

在低能见度条件下对疏散标志的可靠识别对乘员安全至关重要。本研究探讨了色度差异（ΔC *）对视觉识别的影响，并引入了一个为超阈值任务量身定制的基于感知的模型。通过心理物理测试，使用颜色视觉敏锐度（CVA）在不同亮度、色度和色调条件下量化识别性能。结果表明，CVA随感知饱和度的增加而降低，随色调变化显著，特别是在黄色（90°）附近由于s锥灵敏度限制而降低。亮度（L *）在所有条件下都能持续增强CVA。提出了一种新的感知色差（PCD）模型，该模型基于人类色彩敏感度加权的光谱亮度差异。该模型与CVA表现出鲁棒的对数相关性，优于传统的ΔE指标，该指标针对近阈值颜色识别而不是识别进行了优化。建议采用双阈值标准，CVA≥4.0和PCD≥0.0005，以确保在安全关键环境中有效识别。研究结果支持设计更有效的疏散标志，将人类的视觉反应与低能见度环境中的照明条件联系起来。

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引用次数: 0

Assessing compatibility of self-healing nutrients for microbially induced calcium carbonate precipitation in calcium aluminate cement 评估自修复营养素在铝酸钙水泥中微生物诱导碳酸钙沉淀的相容性

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-14 DOI: 10.1016/j.dibe.2026.100849

Daniel Tariku , H.M. Son , Daeik Jang , Solmoi Park

Microcrack formation in concrete poses a risk of durability degradation and ultimate failure of the structure. Calcium aluminate cements (CAC) are susceptible to significant early age microcrack formation due to their rapid setting behavior. Microbially-induced calcium carbonate precipitation (MICP) is a promising research frontier that delivers concrete self-healing capability upon the incorporation of specialized bacteria and nutrients into the cement mix. This study explored the applicability of bacteria-based self-healing technology to mitigate microcrack formation in CAC. The effect of self-healing nutrients such as urea, yeast extract and calcium lactate on the mechanical properties, phase assemblage and reaction kinetics of CAC were investigated. The results demonstrated that the incorporation of self-healing nutrients improved the compressive strength of the specimens by an order of magnitude, ranging from 21 to 97 %. However, the reaction kinetics of CAC was delayed by 25–65 h with the incorporation of yeast extract and calcium lactate.

混凝土微裂缝的形成会造成结构耐久性下降和最终破坏的危险。铝酸钙水泥（CAC）由于其快速凝固的特性，在早期易形成显著的微裂纹。微生物诱导碳酸钙沉淀（MICP）是一个很有前途的研究前沿，通过将特定的细菌和营养物质掺入水泥混合物中，使混凝土具有自修复能力。本研究探讨了基于细菌的自愈技术在缓解CAC微裂纹形成中的适用性。研究了尿素、酵母浸膏和乳酸钙等自愈性营养物对CAC力学性能、相组合和反应动力学的影响。结果表明，自愈性营养物的掺入使试样的抗压强度提高了一个数量级，范围从21%到97% %。然而，酵母提取物和乳酸钙的掺入使CAC的反应动力学延迟了25-65 h。

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引用次数: 0

Interpretable machine learning framework for performance-based retrofit scheme of blast-damaged reinforced concrete columns 基于性能的爆炸损伤钢筋混凝土柱改造方案的可解释机器学习框架

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-12 DOI: 10.1016/j.dibe.2026.100847

Yeeun Kim , Kihak Lee , Jiuk Shin

Explainable artificial intelligence (xAI) has been widely used to improve learning performance because it helps users understand the learning processes. This paper proposes an xAI-based framework to build retrofit schemes for blast-damaged RC columns. This framework includes a multi-stage learner rapidly predicting blast resistance levels using simple structural details. The extensive data for the blast resistance was analyzed with a three-step interpreting process: (1) partial dependence plot (PDP) to initially judge whether the retrofit is effective, (2) 1D accumulated local effect (ALE) to set the quantitative retrofit thresholds for ductility- and stiffness-related variables, and (3) 2D ALE to build effective retrofit schemes considering the interactive effects of retrofit variables on blast resistance. Based on the interpretation results, the various retrofit schemes were recommended for the column failure types and expected damage conditions. Overall, multiple retrofit schemes were required for the columns to accommodate the expected severe and moderate damage conditions.

可解释人工智能（xAI）被广泛用于提高学习性能，因为它可以帮助用户理解学习过程。本文提出了一种基于xai的框架来构建爆炸破坏钢筋混凝土柱的改造方案。该框架包括一个多阶段学习器，使用简单的结构细节快速预测爆炸阻力水平。对大量的爆破阻力数据进行分析，采用三步解释过程：(1)偏相关图（PDP）初步判断改造是否有效；(2)一维累积局部效应（ALE）设定塑性和刚度相关变量的定量改造阈值；(3)二维累积局部效应（ALE）考虑改造变量对爆破阻力的相互作用，建立有效的改造方案。根据解释结果，针对柱的破坏类型和预期损伤情况，推荐了不同的改造方案。总的来说，为了适应预期的严重和中度损坏情况，需要对柱子进行多种改造方案。

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引用次数: 0

Bayesian-optimized CNN-LSTM neural network for predicting road construction dust concentrations 基于贝叶斯优化CNN-LSTM神经网络的道路施工粉尘浓度预测

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-08 DOI: 10.1016/j.dibe.2026.100843

Yilin Wang , Yikun Su , Zhizhe Zheng , Zhichao Zhou , Xing Wang

High dust concentrations from road construction degrade air quality, threaten human health, and increase machinery wear and fuel use. Accurate prediction of dust concentrations is therefore critical for proactive environmental control and low-carbon construction. This study proposes a Bayesian-optimized neural network model that integrates spatial, temporal, and environmental information from multi-source data, including particulate sensors, meteorological parameters, and construction records. The convolutional neural network (CNN) captures spatial features, the long short-term memory (LSTM) learns temporal dependencies, and Bayesian optimization (BO) automatically tunes hyperparameters to enhance prediction performance. The proposed model achieves high accuracy (R² = 0.884) and exhibits superior short-term and long-term robustness compared with conventional models. These results demonstrate that the BO-CNN-LSTM framework effectively improves dust prediction accuracy and stability, providing a practical and intelligent tool for dust mitigation, energy-efficient scheduling, and carbon reduction in road construction projects.

道路建设产生的高浓度粉尘会降低空气质量，威胁人体健康，并增加机械磨损和燃料使用。因此，准确预测粉尘浓度对主动环境控制和低碳建设至关重要。本研究提出了一个贝叶斯优化的神经网络模型，该模型集成了来自多源数据的空间、时间和环境信息，包括颗粒传感器、气象参数和建筑记录。卷积神经网络（CNN）捕获空间特征，长短期记忆（LSTM）学习时间依赖性，贝叶斯优化（BO）自动调整超参数以提高预测性能。与传统模型相比，该模型具有较高的精度（R2 = 0.884），具有较好的短期和长期稳健性。结果表明，BO-CNN-LSTM框架有效提高了扬尘预测的准确性和稳定性，为道路建设项目的降尘、节能调度和减碳提供了实用的智能工具。

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引用次数: 0

Experimental investigation on the influence of drying on the seismic performance of three-story RC buildings 干燥对三层钢筋混凝土建筑抗震性能影响的试验研究

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-08 DOI: 10.1016/j.dibe.2026.100846

Puttipong Srimook , Tatsuya Asai , Masaomi Teshigawara , Pranjal Satya , Ippei Maruyama

The drying of concrete has been recognized as a key phenomenon in the deterioration of concrete structures. Nevertheless, the complex conditions of real RC structures may lead to unforeseen responses observed from existing laboratory experiments on RC members. To clarify the influence of drying, a quasi-static cyclic loading experiment was conducted for one-third scale, three-story RC buildings under wet (saturated) and two-year-dried conditions. The significant decrease in the initial stiffness emphasized the influence of drying on the structural performance regarding residual stress and drying shrinkage cracks affecting the stress-transferring process. In addition, the different deformations of the frame structure indicated the influence of drying on the failure mode. The localized damage occurred early in the wet specimen due to the stress concentration. By contrast, the dried specimen showed only distributed damage during the same cycle. These influences emphasize the impact of drying, which should not be neglected in structural designs.

混凝土的干燥已被认为是混凝土结构劣化的一个关键现象。然而，实际RC结构的复杂条件可能导致从现有的实验室试验中观察到的不可预见的响应。为明确干燥对混凝土结构的影响，对三层混凝土结构进行了三分之一尺度的湿（饱和）和两年干燥条件下的准静态循环加载试验。初始刚度的显著降低强调了干燥对残余应力和干燥收缩裂纹影响应力传递过程的结构性能的影响。此外，不同变形的框架结构表明了干燥对破坏模式的影响。由于应力集中，湿试件的局部损伤较早发生。相比之下，在相同的循环过程中，干燥的试样只表现出分布损伤。这些影响强调干燥的影响，这在结构设计中不应被忽视。

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引用次数: 0

Research on a rapid hidden-line removal and drawing algorithm for large-scale reinforced structures based on geometric parametric representation 基于几何参数表示的大型钢筋结构隐线快速去除与绘制算法研究

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-07 DOI: 10.1016/j.dibe.2025.100842

Wenming Jiang , Ying Zhou , Tianjiao Han , Wang Shen , Fei Han , Yao Wang , Li Jiang

With 2D drawings as the vital bridge between designers and constructors, this study proposes a geometric-parametric hidden-line removal algorithm to resolve the long-standing low efficiency and poor accuracy in generating large-scale rebar component drawings across multiple industries and applications. The method represents each bar with a lightweight “central axis + section parameters” model, reducing geometric complexity by transforming 3D solid intersections into parameter-domain analysis and avoiding the high computational cost of B-rep surface intersections. Curvature-driven adaptive triangulation is employed to accurately extract contours of concrete components, while a BVH based coarse-screening and precise-detection pipeline substantially accelerates occlusion computation. To satisfy engineering drawing standards, the algorithm introduces adaptive offset models for orthogonal and oblique intersection scenarios and incorporates refined treatments for bar ends and bends, ensuring consistent double-line width, and smooth geometric transitions. Experiments on 71 components with varying scales demonstrate that the proposed method requires only 10–30 % of the runtime of the OCC algorithm, achieving 67.14–92.16 % efficiency gains, a mean acceleration factor of 18.10, and a 95 % confidence interval of [15.84, 20.44], with stable performance across large-scale assemblies. The generated drawings meet engineering specifications and significantly reduce manual correction. The proposed approach provides an efficient, controllable, and scalable computational framework for automated drawing generation of large-scale rebar components, with strong transferability to bridge reinforcement, rail-transit pipelines, and other slender-structure applications. Future work may explore integrating the parametric centerline–based visibility determination framework—while preserving its core steps and principles—with AI models such as Random Forest, Neural Implicit Fields (NIF) and PolyDiff Model, enabling more efficient and generalizable hidden-line removal and visibility prediction across complex, cross-domain scenarios.

二维图纸是设计师和施工人员之间的重要桥梁，本研究提出了一种几何参数隐线去除算法，以解决长期以来在多个行业和应用中生成大型螺纹钢构件图纸的效率低、精度差的问题。该方法采用轻量化的“中心轴+截面参数”模型表示每条杆，将三维实体相交转化为参数域分析，降低了几何复杂度，避免了B-rep曲面相交的高计算成本。采用曲率驱动的自适应三角剖分来精确提取混凝土构件的轮廓，而基于BVH的粗筛选和精确检测管道大大加快了遮挡计算。为了满足工程制图标准，该算法引入了正交和斜交场景的自适应偏移模型，并对杆端和弯头进行了精细处理，确保双线宽度一致，几何过渡平滑。在71个不同规模组件上的实验表明，该方法的运行时间仅为OCC算法的10 - 30%，效率提升67.14 - 92.16%，平均加速系数为18.10，置信区间为95%[15.84,20.44]，在大型组件上性能稳定。生成的图纸符合工程规范，大大减少了人工校正。该方法为大规模钢筋构件的自动绘图提供了一个高效、可控和可扩展的计算框架，具有很强的桥梁加固、轨道交通管道和其他细长结构应用的可移植性。未来的工作可能会探索将基于参数中心线的可见性确定框架（同时保留其核心步骤和原则）与随机森林、神经隐式场（NIF）和PolyDiff模型等人工智能模型相结合，从而在复杂的跨域场景中实现更高效、更通用的隐线去除和可见性预测。

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引用次数: 0

Potentials of upcycling Photovoltaic panels waste in construction: A comparative review 建筑中废弃光伏板升级回收的潜力：比较综述

IF 8.2 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Developments in the Built Environment

Pub Date : 2026-01-05 DOI: 10.1016/j.dibe.2026.100845

Omar A. Refaat , Hafiz Asad Ali , Yanshuai Wang , Jian-Guo Dai , Yazan Alrefaei

While photovoltaic (PV) panels drive the global shift to renewable energy, their end-of-life (EoL) disposal (forecast to exceed 78 million tonnes by 2050) poses urgent environmental and resource-recovery challenges. Current management practices, dominated by landfill disposal and low-value recycling, not only result in the loss of valuable elements but also risk leaching toxins. This review critically examines the potential uses of PV waste glass (PVWG) and non-pure PV waste glass (NPVWG) in Portland cement (PC) and alkali-activated material (AAM) systems. Through comparative analysis with conventional waste glass (CWG), the review highlights both shared chemical features yet also distinctive traits of PV panel waste, such as ethylene–vinyl acetate (EVA) layers and metallic residues, which may offer functional advantages in construction applications. Key research gaps are identified in durability performance, hazardous-element immobilization, and processing optimization. The findings set out a targeted research and policy agenda to advance PV waste valorization within a circular-economy framework for the construction sector.

虽然光伏（PV）面板推动了全球向可再生能源的转变，但它们的报废（EoL）处理（预计到2050年将超过7800万吨）带来了紧迫的环境和资源回收挑战。目前以填埋处置和低价值回收为主的管理做法，不仅导致有价值元素的损失，而且有浸出毒素的风险。本文综述了PV废玻璃（PVWG）和非纯PV废玻璃（NPVWG）在硅酸盐水泥（PC）和碱活性材料（AAM）体系中的潜在用途。通过与传统废玻璃（CWG）的对比分析，本文强调了光伏电池板废料的化学特征和独特特征，如乙烯-醋酸乙烯（EVA）层和金属残留物，它们可能在建筑应用中提供功能优势。关键的研究差距确定在耐久性性能，危险元件固定和工艺优化。研究结果提出了一项有针对性的研究和政策议程，以在建筑行业的循环经济框架内推进光伏废弃物的增值。

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引用次数: 0