The accurate prediction of the deterioration of Continuously Reinforced Concrete Pavement(CRCP)is essential for the effective management of pavements and the maintenance of infrastructure.In this study,a comprehensive...The accurate prediction of the deterioration of Continuously Reinforced Concrete Pavement(CRCP)is essential for the effective management of pavements and the maintenance of infrastructure.In this study,a comprehensive approach that integrates descriptive statistics,correlation analysis,and machine learning algorithms is employed to develop models and predict punchouts in CRCP.The dataset used in this study is extracted from the Long-Term Pavement Performance(LTPP)database and contains a wide range of pavement attributes,such as age,climate zone,thickness,and traffic data.Initial exploratory analysis reveals varying distributions among the input features,which serves as the foundation for subsequent analysis.A correlation heatmap matrix is utilized to elucidate the relationships between these attributes and punchouts,guiding the selection of features for modeling.By employing the random forest algorithm,key predictors like age,climate zone,and total thickness are identified.Various machine learning techniques,encompassing linear regression,decision trees,support vector machines,ensemble methods,Gaussian process regression,artificial neural networks,and kernel-based approaches,are compared.It is noteworthy that ensemble methods such as boosted trees and Gaussian process regression models exhibit promising predictive performance,with low root mean square error(RMSE)and high R-squared values.The outcomes of this study provide valuable insights for the development of pavement management strategies,facilitating informed decision-making regarding resource allocation and infrastructure maintenance.Future research could focus on refining models,exploring additional features,and validating results through real-world implementation trials.This study contributes to advancing predictive modeling techniques for optimizing CRCP infrastructure management and durability.展开更多
In this study,green zinc oxide(ZnO)/polypyrrole(Ppy)/cellulose acetate(CA)film has been synthesized via solvent casting.This film was used as supporting material for glucose oxidase(GOx)to sensitize a glucose biosenso...In this study,green zinc oxide(ZnO)/polypyrrole(Ppy)/cellulose acetate(CA)film has been synthesized via solvent casting.This film was used as supporting material for glucose oxidase(GOx)to sensitize a glucose biosensor.ZnO nanoparticles have been prepared via the green route using olive leaves extract as a reductant.ZnO/Ppy nanocomposite has been synthesized by a simple in-situ chemical oxidative polymerization of pyrrole(Py)monomer using ferric chloride(FeCl3)as an oxidizing agent.The produced materials and the composite films were characterized using X-ray diffraction analysis(XRD),scanning electron microscope(SEM),Fourier transform infrared(FTIR)and thermogravimetric analysis(TGA).Glucose oxidase was successfully immobilized on the surface of the prepared film and then ZnO/Ppy/CA/GOx composite was sputtered with platinum electrode for the current determination at different initial concentrations of glucose.Current measurements proved the suitability and the high sensitivity of the constructed biosensor for the detection of glucose levels in different samples.The performance of the prepared biosensor has been assessed by measuring and comparing glucose concentrations up to 800 ppm.The results affirmed the reliability of the developed biosensor towards real samples which suggests the wide-scale application of the proposed biosensor.展开更多
Sewer corrosion is a critical issue that significantly threatens sewer systems,contributing to approximately 40%of sewer infrastructure deterioration.Although numerous review studies have been conducted in this field,...Sewer corrosion is a critical issue that significantly threatens sewer systems,contributing to approximately 40%of sewer infrastructure deterioration.Although numerous review studies have been conducted in this field,gaps persist in identifying the complex factors driving corrosion and understanding their interrelationships.These deficiencies impede the development of accurate corrosion prediction models and the identification of more effective mitigation strategies.This research aims to deepen the understanding of the underlying causes of sewer corrosion,evaluate the latest advancements in prediction models,and explore current mitigation techniques.A novel hybrid approach is employed,combining bibliometric,scientometric,and systematic analysis.While widely used in other fields,this methodology is new in sewer corrosion.The key findings of this study include a comprehensive identification of the various factors influencing corrosion,an overview of existing corrosion prediction models,and an evaluation of currently employed mitigation strategies.Additionally,this research highlights critical research gaps and suggests future avenues for investigation,with the potential to support municipalities in more efficient and flexible management of sewer infrastructure.展开更多
Maintaining the integrity of sewage networks is crucial for sustainable urban development.Despite extensive research on inspection tools,machine learning applications,and condition assessment for sewer defects,a holis...Maintaining the integrity of sewage networks is crucial for sustainable urban development.Despite extensive research on inspection tools,machine learning applications,and condition assessment for sewer defects,a holistic review of these elements remains absent.This paper addresses this gap by presenting a comprehensive review within a unified framework,employing a mixed-method approach that includes bibliometric,scientometric,and systematic analyses.Our findings reveal that integrating in-pipe and out-pipe inspection methods enhances outcomes.The current literature identifies modified RegNet,dilation segmentation with conditional random field(DilaSeg-CRF),you only look once(YOLO)models,and faster region-based convolutional neural network(Faster R-CNN)as effective algorithms for classification,segmentation,and object detection,both on-site and off-site,respectively.However,machine learning is an evolving field,and future algorithms may surpass these models.Identifying key challenges,we propose recommendations aimed at advancing research and enhancing replicability:notably,the expansion of international research collaborations,particularly in underrepresented regions such as the Middle East,Africa,Asia,and South America;applying the latest version of YOLOv11 in object detection;and investigating defect patterns in polyvinyl chloride(PVC)sewer and rehabilitated pipes using advanced diagnostic methods.This review anticipates aiding policymakers in adopting informed strategies,thereby contributing to the development of smarter,more sustainable cities.展开更多
The rapid expansion of both the global economy and the human population has led to a shortage of water resources suitable for direct human consumption.As a result,water remediation will inexorably become the primary f...The rapid expansion of both the global economy and the human population has led to a shortage of water resources suitable for direct human consumption.As a result,water remediation will inexorably become the primary focus on a global scale.Microalgae can be grown in various types of wastewaters(WW).They have a high potential to remove contaminants from the effluents of industries and urban areas.This review focuses on recent advances on WW remediation through microalgae cultivation.Attention has already been paid to microalgae-based wastewater treatment(WWT)due to its low energy requirements,the strong ability of microalgae to thrive under diverse environmental conditions,and the potential to transform WW nutrients into high-value compounds.It turned out that microalgae-based WWT is an economical and sustainable solution.Moreover,different types of toxins are removed by microalgae through biosorption,bioaccumulation,and biodegradation processes.Examples are toxins from agricultural runoffs and textile and pharmaceutical industrial effluents.Microalgae have the potential to mitigate carbon dioxide and make use of the micronutrients that are present in the effluents.This review paper highlights the application of microalgae in WW remediation and the remediation of diverse types of pollutants commonly present in WW through different mechanisms,simultaneous resource recovery,and efficient microalgae-based co-culturing systems along with bottlenecks and prospects.展开更多
This study proposed a novel technique to solve the problem of color distortion in the fusion of the GeoEye-1 satellite’s panchromatic(PAN)and multispectral(MS)images.This technique suggested reducing the difference i...This study proposed a novel technique to solve the problem of color distortion in the fusion of the GeoEye-1 satellite’s panchromatic(PAN)and multispectral(MS)images.This technique suggested reducing the difference in radiometry between the PANandMSimages by usingmodification coefficients for theMS bands in the definition of the intensity(I)equation,which guarantees using only the overlapped wavelengths with the PAN band.Thesemodification coefficients achieve spatiotemporal transferability for the proposed fusion technique.As the reflectance of vegetation is high in the NIR band and low in the RGB bands,this technique suggested using an additional coefficient for the NIR band in the definition of the I equation,which varies based on the ratio of the agricultural features within the image,to indicate the correct impact of vegetation.This vegetation coefficient provides stability for the proposed fusion technique across all land cover classes.This study used three datasets of GeoEye-1 satellite PAN and MS images in Tanta City,Egypt,with different land cover classes(agricultural,urban,and mixed areas),to evaluate the performance of this technique against five different standard image fusion techniques.In addition,it was validated using six additional datasets from different locations and acquired at different times to test its spatiotemporal transferability.The proposed fusion technique demonstrated spatiotemporal transferability as well as great efficiency in producing fused images of superior spatial and spectral quality for all types of land cover.展开更多
文摘The accurate prediction of the deterioration of Continuously Reinforced Concrete Pavement(CRCP)is essential for the effective management of pavements and the maintenance of infrastructure.In this study,a comprehensive approach that integrates descriptive statistics,correlation analysis,and machine learning algorithms is employed to develop models and predict punchouts in CRCP.The dataset used in this study is extracted from the Long-Term Pavement Performance(LTPP)database and contains a wide range of pavement attributes,such as age,climate zone,thickness,and traffic data.Initial exploratory analysis reveals varying distributions among the input features,which serves as the foundation for subsequent analysis.A correlation heatmap matrix is utilized to elucidate the relationships between these attributes and punchouts,guiding the selection of features for modeling.By employing the random forest algorithm,key predictors like age,climate zone,and total thickness are identified.Various machine learning techniques,encompassing linear regression,decision trees,support vector machines,ensemble methods,Gaussian process regression,artificial neural networks,and kernel-based approaches,are compared.It is noteworthy that ensemble methods such as boosted trees and Gaussian process regression models exhibit promising predictive performance,with low root mean square error(RMSE)and high R-squared values.The outcomes of this study provide valuable insights for the development of pavement management strategies,facilitating informed decision-making regarding resource allocation and infrastructure maintenance.Future research could focus on refining models,exploring additional features,and validating results through real-world implementation trials.This study contributes to advancing predictive modeling techniques for optimizing CRCP infrastructure management and durability.
文摘In this study,green zinc oxide(ZnO)/polypyrrole(Ppy)/cellulose acetate(CA)film has been synthesized via solvent casting.This film was used as supporting material for glucose oxidase(GOx)to sensitize a glucose biosensor.ZnO nanoparticles have been prepared via the green route using olive leaves extract as a reductant.ZnO/Ppy nanocomposite has been synthesized by a simple in-situ chemical oxidative polymerization of pyrrole(Py)monomer using ferric chloride(FeCl3)as an oxidizing agent.The produced materials and the composite films were characterized using X-ray diffraction analysis(XRD),scanning electron microscope(SEM),Fourier transform infrared(FTIR)and thermogravimetric analysis(TGA).Glucose oxidase was successfully immobilized on the surface of the prepared film and then ZnO/Ppy/CA/GOx composite was sputtered with platinum electrode for the current determination at different initial concentrations of glucose.Current measurements proved the suitability and the high sensitivity of the constructed biosensor for the detection of glucose levels in different samples.The performance of the prepared biosensor has been assessed by measuring and comparing glucose concentrations up to 800 ppm.The results affirmed the reliability of the developed biosensor towards real samples which suggests the wide-scale application of the proposed biosensor.
基金supported by the Research Grants Council of the University Grants Committee in Hong Kong,China(No.RGC-15209022).
文摘Sewer corrosion is a critical issue that significantly threatens sewer systems,contributing to approximately 40%of sewer infrastructure deterioration.Although numerous review studies have been conducted in this field,gaps persist in identifying the complex factors driving corrosion and understanding their interrelationships.These deficiencies impede the development of accurate corrosion prediction models and the identification of more effective mitigation strategies.This research aims to deepen the understanding of the underlying causes of sewer corrosion,evaluate the latest advancements in prediction models,and explore current mitigation techniques.A novel hybrid approach is employed,combining bibliometric,scientometric,and systematic analysis.While widely used in other fields,this methodology is new in sewer corrosion.The key findings of this study include a comprehensive identification of the various factors influencing corrosion,an overview of existing corrosion prediction models,and an evaluation of currently employed mitigation strategies.Additionally,this research highlights critical research gaps and suggests future avenues for investigation,with the potential to support municipalities in more efficient and flexible management of sewer infrastructure.
基金supported by the Research Grants Council of the University Grants Committee(Grant No.RGC-15209022)the General Research Fund(Grant No.GRF-15202524)in Hong Kong,China.
文摘Maintaining the integrity of sewage networks is crucial for sustainable urban development.Despite extensive research on inspection tools,machine learning applications,and condition assessment for sewer defects,a holistic review of these elements remains absent.This paper addresses this gap by presenting a comprehensive review within a unified framework,employing a mixed-method approach that includes bibliometric,scientometric,and systematic analyses.Our findings reveal that integrating in-pipe and out-pipe inspection methods enhances outcomes.The current literature identifies modified RegNet,dilation segmentation with conditional random field(DilaSeg-CRF),you only look once(YOLO)models,and faster region-based convolutional neural network(Faster R-CNN)as effective algorithms for classification,segmentation,and object detection,both on-site and off-site,respectively.However,machine learning is an evolving field,and future algorithms may surpass these models.Identifying key challenges,we propose recommendations aimed at advancing research and enhancing replicability:notably,the expansion of international research collaborations,particularly in underrepresented regions such as the Middle East,Africa,Asia,and South America;applying the latest version of YOLOv11 in object detection;and investigating defect patterns in polyvinyl chloride(PVC)sewer and rehabilitated pipes using advanced diagnostic methods.This review anticipates aiding policymakers in adopting informed strategies,thereby contributing to the development of smarter,more sustainable cities.
基金supported by the National Natural Science Foundation of China(31772529)the National Key R&D Program of China(2018YFE0107100)the Priority of Academic Program Development of Jiangsu Higher Education Institutions(PAPD 4013000011).
文摘The rapid expansion of both the global economy and the human population has led to a shortage of water resources suitable for direct human consumption.As a result,water remediation will inexorably become the primary focus on a global scale.Microalgae can be grown in various types of wastewaters(WW).They have a high potential to remove contaminants from the effluents of industries and urban areas.This review focuses on recent advances on WW remediation through microalgae cultivation.Attention has already been paid to microalgae-based wastewater treatment(WWT)due to its low energy requirements,the strong ability of microalgae to thrive under diverse environmental conditions,and the potential to transform WW nutrients into high-value compounds.It turned out that microalgae-based WWT is an economical and sustainable solution.Moreover,different types of toxins are removed by microalgae through biosorption,bioaccumulation,and biodegradation processes.Examples are toxins from agricultural runoffs and textile and pharmaceutical industrial effluents.Microalgae have the potential to mitigate carbon dioxide and make use of the micronutrients that are present in the effluents.This review paper highlights the application of microalgae in WW remediation and the remediation of diverse types of pollutants commonly present in WW through different mechanisms,simultaneous resource recovery,and efficient microalgae-based co-culturing systems along with bottlenecks and prospects.
文摘This study proposed a novel technique to solve the problem of color distortion in the fusion of the GeoEye-1 satellite’s panchromatic(PAN)and multispectral(MS)images.This technique suggested reducing the difference in radiometry between the PANandMSimages by usingmodification coefficients for theMS bands in the definition of the intensity(I)equation,which guarantees using only the overlapped wavelengths with the PAN band.Thesemodification coefficients achieve spatiotemporal transferability for the proposed fusion technique.As the reflectance of vegetation is high in the NIR band and low in the RGB bands,this technique suggested using an additional coefficient for the NIR band in the definition of the I equation,which varies based on the ratio of the agricultural features within the image,to indicate the correct impact of vegetation.This vegetation coefficient provides stability for the proposed fusion technique across all land cover classes.This study used three datasets of GeoEye-1 satellite PAN and MS images in Tanta City,Egypt,with different land cover classes(agricultural,urban,and mixed areas),to evaluate the performance of this technique against five different standard image fusion techniques.In addition,it was validated using six additional datasets from different locations and acquired at different times to test its spatiotemporal transferability.The proposed fusion technique demonstrated spatiotemporal transferability as well as great efficiency in producing fused images of superior spatial and spectral quality for all types of land cover.
