Search Results (238)

Over efficient photocatalysts, CO₂ photoreduction typically converts CO₂ into low-carbon chemicals, which serve as raw materials for downstream synthesis processes. Here, an efficient composite photocatalyst heterojunction (Cu₂O-u/g-C₃N₄) has been fabricated to reduce CO₂. Graphitic carbon nitride (g-C₃N₄) was synthesized via thermal polymerization of urea at 550 °C, while pre-dispersed Cu₂O derived from urea pyrolysis (Cu₂O-u) was prepared by thermal reduction of urea and CuCl₂·2H₂O at 180 °C. The heterojunction Cu₂O-u/g-C₃N₄ was subsequently constructed through hydrothermal treatment at 180 °C. This heterojunction exhibited a bandgap of 2.10 eV, with dual optical absorption edges at 485 nm and above 800 nm, enabling efficient harvesting of solar light. Under 175 W mercury lamp irradiation, the heterojunction catalyzed liquid-phase CO₂ photoreduction to formic acid, acetic acid, and methanol. Its formic acid production activity surpassed that of pristine g-C₃N₄ by 3.14-fold and TiO₂ by 8.72-fold. Reaction media, hole scavengers, and reaction duration modulated product selectivity. In acetonitrile/isopropanol systems, formic acid and acetic acid production reached 579.4 and 582.8 μmol·h⁻¹·g_cat⁻¹. Conversely, in water/triethanolamine systems, methanol production reached 3061.6 μmol·h⁻¹·g_cat⁻¹, with 94.79% of the initial conversion retained after three cycles. Finally, this work ends with the conclusions of the CO₂ photocatalytic reduction to formic acid, acetic acid, and methanol, and recommends prospects for future research. Full article

As a novel prefabricated structural element, the pre-tensioned, prestressed concrete T-beam with polygonal tendons layout demonstrates advantages including reduced prestress loss, streamlined construction procedures, and stable manufacturing quality, showing promising applications in medium-span bridge engineering. This paper conducted a full-scale experiment and numerical simulation research on a 30 m pre-tensioned, prestressed concrete T-beam with polygonal tendons practically used in engineering. The full-scale experiment applied symmetrical four-point bending to create a pure bending region and used embedded strain gauges, surface sensors, and optical 3D motion capture systems to monitor the beam’s internal strain, surface strain distribution, and three-dimensional displacement patterns during loading. The experiment observed that the test beam underwent elastic, crack development, and failure phases. The design’s service-load bending moment induced a deflection of 18.67 mm (below the 47.13 mm limit). Visible cracking initiated under a bending moment of 7916.85 kN·m, which exceeded the theoretical cracking moment of 5928.81 kN·m calculated from the design parameters. Upon yielding of the bottom steel reinforcement, the maximum of the crack width reached 1.00 mm, the deflection in mid-span measured 148.61 mm, and the residual deflection after unloading was 10.68 mm. These results confirmed that the beam satisfied design code requirements for serviceability stiffness and crack control, exhibiting favorable elastic recovery characteristics. Numerical simulations using ABAQUS further verified the structural performance of the T-beam. The finite element model accurately captured the beam’s mechanical response and verified its satisfactory ductility, highlighting the applicability of this beam type in bridge engineering. Full article

Urban regeneration, as a key strategy for promoting sustainable development of urban areas, requires innovative digital technologies to address increasingly complex urban challenges in its implementation. With the fast advancement of digital technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data, these technologies have extensively penetrated various dimensions of urban regeneration, from planning and design to implementation and post-operation management, providing new possibilities for improving urban regeneration efficiency and quality. However, the existing literature lacks a systematic evaluation of technology application patterns across different project scales and phases, comprehensive analysis of stakeholder–technology interactions, and quantitative assessment of technology distribution throughout the urban regeneration lifecycle. This research gap limits the in-depth understanding of how digital technologies can better support urban regeneration practices. This study aims to identify and quantify digital technology application patterns across urban regeneration stages, scales, and stakeholder configurations through systematic analysis of 56 high-quality articles from the Scopus and Web of Science databases. Using a mixed-methods approach combining a systematic literature review, bibliometric analysis, and meta-analysis, we categorized seven major digital technology types and analyzed their distribution patterns. Key findings reveal distinct temporal patterns: GIS and BIM/CIM technologies dominate in the pre-urban regeneration (Pre-UR) stage (10% and 12% application proportions, respectively). GIS applications increase significantly to 14% in post-urban regeneration (Post-UR) stage, while AI technology remains underutilized across all phases (2% in Pre-UR, decreasing to 1% in Post-UR). Meta-analysis reveals scale-dependent technology adoption patterns, with different technologies showing varying effectiveness at building-level, district-level, and city-level implementations. Research challenges include stakeholder digital divides, scale-dependent adoption barriers, and phase-specific implementation gaps. This study constructs a multi-dimensional analytical framework for digital technology support in urban regeneration, providing quantitative evidence for optimizing technology selection strategies. The framework offers practical guidance for policymakers and practitioners in developing context-appropriate digital technology deployment strategies for urban regeneration projects. Full article

This paper outlines a structural qualification process to assess the use of newly developed high-modulus (HM) glass fiber-reinforced polymer (GFRP) bars with headed ends in the joint between concrete bridge barriers and decks. The main goals of the study are to evaluate the structural performance of GFRP-reinforced TL-5 barrier–deck systems under transverse loading and to determine the pullout capacity of GFRP anchorage systems for both new construction and retrofit applications. The research is divided into two phases. In the first phase, six full-scale Test-Level 5 (TL-5) barrier wall–deck specimens, divided into three systems, were constructed and tested up to failure. The first system used headed-end GFRP bars to connect the barrier wall to a non-deformable thick deck slab. The second system was similar to the first but had a deck slab overhang for improved anchorage. The third system utilized postinstalled GFRP bars in a non-deformable thick deck slab, bonded with a commercial epoxy adhesive as a solution for deteriorated barrier replacement. The second phase involves an experimental program to evaluate the pullout strength of the GFRP bar anchorage in normal-strength concrete. The experimental results from the tested specimens were then compared to the factored applied moments in existing literature based on traffic loads in the Canadian Highway Bridge Design Code. Experimental results confirmed that GFRP-reinforced TL-5 barrier–deck systems exceeded factored design moments, with capacity-to-demand ratios above 1.38 (above 1.17 with the inclusion of an environmental reduction factor of 0.85). A 195 mm embedment length proved sufficient for both pre- and postinstalled bars. Headed-end GFRP bars improved pullout strength compared to straight-end bars, especially when bonded. Failure modes occurred at high loads, demonstrating structural integrity. Postinstalled bars bonded with epoxy performed comparably to preinstalled bars. A design equation for the barrier resistance due to a diagonal concrete crack at the barrier–deck corner was developed and validated using experimental findings. This equation offers a conservative and safe design approach for evaluating barrier–deck anchorage. Full article

Evaluating how dams modify hydrological regimes and their long-term impacts on riverine ecosystems is critical. This study evaluated trends and change points in Fractional Vegetation Cover (FVC) of braided riverbanks downstream of the Xiaolangdi Dam (1990–2020) using time-series decomposition and structural breakpoint analysis. Distinct temporal periods corresponding to different dam construction and operational phases were identified. Partial correlation analysis and linear mixed-effects modeling were employed to elucidate the spatiotemporal linkages between FVC and key driving factors. The results identified 1997 and 2004 as significant change points in FVC, corresponding to the dam’s construction and initial interception in 1997, and its subsequent comprehensive water and sediment regulation from 2004 onwards, respectively. Although dam construction may have initially posed short-term challenges to downstream vegetation, the post-operational phase witnessed a notable increase in significant vegetation growth compared to the pre-dam period, primarily attributed to the altered hydrological conditions. Notably, the dam operation’s contribution to the total FVC increase was 56% in the near-dam Xiaolangdi–Jiahetan reach. The analysis revealed distinct differences in vegetation responses to these hydrological alterations between the upstream Xiaolangdi–Jiahetan and downstream Jiahetan–Gaocun river sections, with the latter demonstrating greater ecological sensitivity to the dam-induced hydrological changes. Full article

Evaluating the environmental perception of urban parks is highly significant for optimizing urban planning. To address the limitations of traditional evaluation methods, a multimodal deep learning framework that integrates pre-training and reinforcement learning strategies for the comprehensive assessment of various park types (seaside, urban, mountain, and wetland) across three dimensions—accessibility, usability, and aesthetics—is proposed herein. By combining image data and user review texts, a unified architecture is constructed, including a text encoder, image visual encoder, and multimodal fusion module. During the pre-training phase, the model captured latent features in images and texts through a self-supervised learning strategy. In the subsequent training phase, a reinforcement learning strategy was introduced to optimize the sample selection and modal fusion paths to enhance the model’s generalization capability. To validate the cross-type prediction ability of the model, the experimental design uses data from three types of parks for training, with the remaining type as a test set. Results demonstrate that the proposed method outperforms LSTM and CNN architectures across accuracy, precision, recall, and F1 Score metrics. Compared with CNN, the proposed method improves accuracy by 5.1% and F1 Score by 6.6%. Further analysis shows that pre-training enhances the robust fusion of visual and textual features, while reinforcement learning optimizes the sample selection and feature fusion strategies during training. Full article

Some construction projects in Indonesia have been experiencing frequent major delays, and there is an urgent need to perform a comprehensive schedule forecast to anticipate them. This study aims to enhance the forecast of the project-completion schedule of a major railway construction project in Indonesia, with considerations of the identified project risks. Using structured interviews of practitioners and experts, this study has identified key risks in the Phase II Construction project. The @RISK V8.5.1 software was used to run Monte Carlo Simulation to calculate the scheduling forecast more accurately with the PERT method. The results of the scheduling simulation on the inherent key risks that affect key activities in the critical path show that there are additional days due to pre-mitigation during the construction of the project, ranging from 130.81% to 136.25% of the initial contract completion duration to approximately 95.82% to 102.23% of the original contract completion duration. This research provides a way not only to identify and prioritize the risks but also to quantify them to estimate the project completion schedule more up to 95% accuracy using Monte Carlo Simulation, where the model can be used to plan and monitor the risks of future projects. Full article

Delays in road construction projects pose significant challenges in the Philippines, resulting in increased costs, project overruns, and unmet infrastructure goals. Common causes include poor financial management, inadequate subcontractor performance, deficient planning, and regulatory bottlenecks. This study aims to develop a comprehensive and data-driven framework to mitigate construction delays using the Analytical Hierarchy Process (AHP). The methodology integrates literature review, expert surveys, and pairwise comparisons to identify and prioritize critical delay factors. Experts from the Department of Public Works and Highways (DPWH), private contractors, and academia contributed to the AHP model. The results highlight seven major factor groups: client-related, contractor-related, consultant-related, materials, labor and equipment, contractual issues, and external influences. AHP analysis identified financial management, planning and scheduling, and regulatory coordination as the most impactful causes. Based on these findings, a strategic framework was developed and visualized using a Fishbone Diagram to present mitigation strategies tailored to each factor. While environmental engineering principles—such as material efficiency, energy use optimization, and impact assessments—are acknowledged, they serve as guiding themes rather than formal components of the framework. The study offers practical, stakeholder-validated recommendations for both pre- and post-construction phases, including real-time monitoring, risk anticipation, and improved multi-agency coordination. This framework provides a scalable tool for DPWH and related agencies to improve infrastructure delivery while supporting long-term sustainability goals. Full article

The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations from Space (WOfS) platform, to quantify the hydrological impacts of GERD’s three filling phases (2019–2022) on Sudan’s Roseires Dam. Using Sentinel-2 satellite data processed through the Open Data Cube framework, we analyzed water extent changes from 2018 to 2023, capturing pre- and post-filling dynamics. Results show that GERD’s water spread area increased from 80 km² in 2019 to 528 km² in 2022, while Roseires Dam’s water extent decreased by 9 km² over the same period, with a notable 5 km² loss prior to GERD’s operation (2018–2019). These changes, validated against PERSIANN-CDR rainfall data, correlate with GERD’s filling operations, alongside climatic factors like evapotranspiration and reduced rainfall. The study highlights the potential of Earth Observation (EO) technologies to support transparent, data-driven transboundary water governance. Despite the Cooperative Framework Agreement (CFA) ratified by six upstream states in 2024, mistrust persists due to Egypt and Sudan’s non-ratification. We propose enhancing the Nile Basin Initiative’s Decision Support System with EO data and AI-driven models to optimize water allocation and foster cooperative filling strategies. Benefit-sharing mechanisms, such as energy trade from GERD, could mitigate downstream losses, aligning with the CFA’s equitable utilization principles and the UN Watercourses Convention. This research underscores the critical role of EO-driven frameworks in resolving Nile Basin conflicts and achieving Sustainable Development Goal 6 for sustainable water management. Full article

Traditional price prediction of construction material concrete often adopts macroeconomic indicators as independent variables. However, since there is often a closer relationship between the raw materials of construction concrete and the production of construction materials, the price prediction of construction concrete based on raw material prices can more directly ensure the prediction accuracy. Therefore, this study proposes a Double-Branch Physics-Informed Neural Network (DB-PINN) model based on both macroeconomic indicators and raw material price factors for the construction concrete price prediction. In particular, this model utilizes an Artificial Neural Network (ANN) as the baseline algorithm and incorporates physical constraints, such as a Multiple Linear Regression (MLR) model and a Vector Error Correction Model (VECM) to modify the loss function. To improve the prediction accuracy of the DB-PINN model, a feature analysis of the effect of the raw material price factors on the construction concrete price is conducted. Results showed that the proposed DB-PINN model has high accuracy in concrete price prediction. Further, to explore the specific ways in which macroeconomic indicators affect the concrete price prediction, a Marginal Effect Analysis (MEA) is conducted. Moreover, a comparative analysis using a traditional ANN model is conducted to verify the efficiency of the DB-PINN model, and a parameter sensitivity analysis is performed to reveal the impact of each raw material price factor and macroeconomic indicator on the construction concrete price. This study incorporates the introduction of raw material prices as input parameters for construction concrete price prediction, which facilitates the development of urban construction concrete price management in the pre-project phase. Full article

By reconstructing the concept of the “Mandate of Heaven”, the Zisi School grounded the universality of Confucian ethics in the ontological stipulations of Heaven’s Way, bridging the intellectual gap between Confucius’s practical ethics and Mencius’s theory of mind-nature. Central to their framework is the proposition that “Heaven’s mold imparts form to mankind; and imparts inherent pattern to objects”, which constructs a generative chain from the Mandate of Heaven to the nature of objects and human nature. The School posited that the Heavenly Way endows all objects with inherent patterns, while human nature, derived from the Mandate of Heaven, harbors latent moral potential activated through edification. By dialectically reconciling the “differentiation between Heaven and humans” with the “unity of Heaven and humanity”, the Zisi School emphasized both the transcendent authority of the Mandate of Heaven and human moral agency, forming an “immanent yet transcendent” ethical paradigm. However, theoretical limitations persist, including ambiguities in the certainty of innate goodness due to the separation of Heaven and human nature, mind-body dualism that risks formalizing moral practice, and latent fatalism in their concept of mandate. Despite these unresolved tensions, the Zisi School’s metaphysics laid the groundwork for Mencius’s theory of innate goodness, Xunzi’s legalist emphasis on ritual, and Song-Ming Neo-Confucian discourses on “Heaven’s inherent pattern”. As a pivotal transitional phase in Pre-Qin Confucianism, the Zisi School highlights the interplay between metaphysical grounding and pragmatic adaptability, underscoring the enduring dynamism of Confucian ethics. Full article

In this study, the effect of plastic residual strain on the corrosion behavior of ZK60 magnesium alloy was systematically revealed using a research method combining experimental characterization and numerical simulation. Based on the multiphysical field coupling theory, a numerical model containing deformation field, corrosion phase field, and material transfer field was constructed, and the dynamic simulation of plastic residual strain-induced corrosion damage was successfully realized. Tafel polarization curves obtained from electrochemical tests were fitted to the key parameters of the secondary current distribution. The kinetic parameter L controlling the corrosion rate in the phase-field model was innovatively determined by the inverse calibration method, and a quantitative relationship between the kinetics of electrochemical corrosion and the phase-field theory was established. The corrosion depth distribution of the pre-strained specimens is quantitatively characterized and the results are in agreement with the finite element simulation results. The coupled strain-corrosion analysis method proposed in this study provides a theoretical basis for the design and life prediction of corrosion resistance of components under complex stress states. Full article

Automation is revolutionizing a number of sectors, including construction, by bringing about important technological breakthroughs that increase productivity and efficiency. Automation in safety procedures is still scarce though. In India, the majority of safety procedures are still reactive, manual, and paper-based. This study is a component of a broader research project on automated safety screening for fall risks enabled by BIM. It entails codification of OSHA rules to perform safety checks, placing corrective actions into location, and generating reports in a virtual environment. As part of the broader risk lifecycle, these tasks are typically completed on-site during the various stages of construction. This study, on the other hand, executes these steps in a virtual environment in the preconstruction phase. The model has been assessed in a pilot study in India and was developed especially to address fall hazards from staircases. Through early hazard identification and mitigation, the system assists professionals in enhancing overall safety performance. Full article

The granitic rocks and the Dokhan Volcanics at the transect between Safaga and Qena, the Egyptian Northern Eastern Desert represent the northern termination of the Arabian–Nubian Shield (ANS), which, in turn, represents the northern part of the East African Orogeny (EAO). The geochronological development of the magmatic activities that constructed the ANS is critical in understanding these orogenies. The ANS was constructed through pre-collisional, syn-collisional, and post-collisional magmatic phases. The transition between these magmatic phases marks tectonic shifting from subduction to compressional and extensional tectonic settings, respectively. The chronological constraints of these tectonic–magmatic phases are still questionable. Our study aims to refine these chronological constraints through the dating of four calc-alkaline granitic rocks (722 ± 5 Ma–561 ± 4 Ma), five alkaline granitic rocks (758 ± 5 Ma–555 ± 4 Ma), and three Dokhan Volcanic rocks (618 ± 5 Ma–606 ± 5 Ma). Our results suggest the absence of any pre-collisional rocks. The syn-collisional magmatism extended here from 758 ± 5 Ma to 653 ± 7 Ma, demonstrating the chronological domination of the syn-orogenic compressional regime in the NED. The Dokhan Volcanic activity marked the shifting of the tectonic setting from a compressional to an extensional regime at 618 ± 5 Ma. Post-collisional plutonism dominated between 583 ± 5 Ma and 555 ± 4 Ma in the studied region, suggesting that ANS magmatic activity was extended to the Phanerozoic edge. These findings refute the classical interpretations of older magmatism as calc-alkaline granitoids and younger magmatism as alkaline granitoids. Pre-Neoproterozoic (pre-ANS) xenocrysts with ages of 1879 ± 22, 1401 ± 25, 1385 ± 12, 1232 ± 27, 1210 ± 18, and 1130 ± 15 Ma were yielded, which might support a local reworked ancient magmatic source. Full article

The characterization of historical tailings bodies is crucial for optimizing environmental management and resource recovery efforts. This study investigated the Bielatal tailings dam (Altenberg, Germany), examining its internal structure, material distribution influenced by historical flushing technology, and the spatial distribution of valuable elements. To evaluate the tailings resource potential, drill core sampling was conducted at multiple points at a depth of 7 m. Subsequent analyses included geochemical characterization using sodium peroxide fusion, lithium borate fusion, X-ray fluorescence (XRF), and a scanning electron microscope with energy dispersive X-ray spectroscopy (SEM-EDX). Particle size distribution analysis via a laser particle size analyzer and wet sieving was conducted alongside milieu parameter (pH, Eh, EC) analysis. A theoretical assessment of the tailings’ potential for geopolymer applications was conducted by comparing them with other tailings used in geopolymer research and relevant European standards. The results indicated average concentrations of lithium (Li) of 0.1 wt%, primarily hosted in Li-mica phases, and concentrations of tin (Sn) of 0.12 wt%, predominantly occurring in cassiterite. Particle size analysis revealed that the tailings material is generally fine-grained, comprising approximately 60% silt, 32% fine sand, and 8% clay. These textural characteristics influenced the spatial distribution of elements, with Li and Sn enriched in fine-grained fractions predominantly concentrated in the dam’s central and western sections, while coarser material accumulated near injection points. Historical advancements in mineral processing, particularly flotation, had significantly influenced Sn distribution, with deeper layers showing higher Sn enrichment, except for the final operational years, which also exhibited elevated Sn concentrations. Due to the limitations of X-ray fluorescence (XRF) in detecting Li, a strong correlation between rubidium (Rb) and Li was established, allowing Li quantification via Rb measurements across varying particle sizes, redox conditions, and geological settings. This demonstrated that Rb can serve as a reliable proxy for Li quantification in diverse contexts. Geochemical and mineralogical analyses revealed a composition dominated by quartz, mica, topaz, and alkali feldspars. The weakly acidic to neutral conditions (pH 5.9–7.7) and reducing redox potential (Eh, 570 to 45 mV) of the tailings material indicated a minimal risk of acid mine drainage. Preliminary investigations into using Altenberg tailings as geopolymer materials suggested that their silicon-rich composition could serve as a substitute for coal fly ash in construction; however, pre-treatment would be needed to enhance reactivity. This study underscores the dual potential of tailings for element recovery and sustainable construction, emphasizing the importance of understanding historical processing techniques for informed resource utilization. Full article