Pub Date : 2024-01-06DOI: 10.3390/infrastructures9010011
B. Pulatsu, R. Wilson, José V. Lemos, N. Mojsilović
Unreinforced masonry (URM) walls are common load-bearing structural elements in most existing buildings, consisting of masonry units (bricks) and mortar joints. They indicate a highly nonlinear and complex behaviour when subjected to combined compression–shear loading influenced by different factors, such as pre-compression load and boundary conditions, among many others, which makes predicting their structural response challenging. To this end, the present study offers a discontinuum-based modelling strategy based on the discrete element method (DEM) to investigate the in-plane cyclic response of URM panels under different vertical pressures with and without a damp-proof course (DPC) membrane. The adopted modelling strategy represents URM walls as a group of discrete rigid block systems interacting along their boundaries through the contact points. A novel contact constitutive model addressing the elasto-softening stress–displacement behaviour of unit–mortar interfaces and the associated stiffness degradation in tension–compression regimes is adopted within the implemented discontinuum-based modelling framework. The proposed modelling strategy is validated by comparing a recent experimental campaign where the essential data regarding geometrical features, material properties and loading histories are obtained. The results show that while the proposed computational modelling strategy can accurately capture the hysteric response of URM walls without a DPC membrane, it may underestimate the load-carrying capacity of URM walls with a DPC membrane.
{"title":"Exploring the Cyclic Behaviour of URM Walls with and without Damp-Proof Course (DPC) Membranes through Discrete Element Method","authors":"B. Pulatsu, R. Wilson, José V. Lemos, N. Mojsilović","doi":"10.3390/infrastructures9010011","DOIUrl":"https://doi.org/10.3390/infrastructures9010011","url":null,"abstract":"Unreinforced masonry (URM) walls are common load-bearing structural elements in most existing buildings, consisting of masonry units (bricks) and mortar joints. They indicate a highly nonlinear and complex behaviour when subjected to combined compression–shear loading influenced by different factors, such as pre-compression load and boundary conditions, among many others, which makes predicting their structural response challenging. To this end, the present study offers a discontinuum-based modelling strategy based on the discrete element method (DEM) to investigate the in-plane cyclic response of URM panels under different vertical pressures with and without a damp-proof course (DPC) membrane. The adopted modelling strategy represents URM walls as a group of discrete rigid block systems interacting along their boundaries through the contact points. A novel contact constitutive model addressing the elasto-softening stress–displacement behaviour of unit–mortar interfaces and the associated stiffness degradation in tension–compression regimes is adopted within the implemented discontinuum-based modelling framework. The proposed modelling strategy is validated by comparing a recent experimental campaign where the essential data regarding geometrical features, material properties and loading histories are obtained. The results show that while the proposed computational modelling strategy can accurately capture the hysteric response of URM walls without a DPC membrane, it may underestimate the load-carrying capacity of URM walls with a DPC membrane.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"47 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.3390/infrastructures9010010
B. Patra, Rocio L. Segura, Ashutosh Bagchi
This study addresses the vital issue of the variability associated with modeling decisions in dam seismic analysis. Traditionally, structural modeling and simulations employ a progressive approach, where more complex models are gradually incorporated. For example, if previous levels indicate insufficient seismic safety margins, a more advanced analysis is then undertaken. Recognizing the constraints and evaluating the influence of various methods is essential for improving the comprehension and effectiveness of dam safety assessments. To this end, an extensive parametric study is carried out to evaluate the seismic response variability of the Koyna and Pine Flat dams using various solution approaches and model complexities. Numerical simulations are conducted in a 2D framework across three software programs, encompassing different dam system configurations. Additional complexity is introduced by simulating reservoir dynamics with Westergaard-added mass or acoustic elements. Linear and nonlinear analyses are performed, incorporating pertinent material properties, employing the concrete damage plasticity model in the latter. Modal parameters and crest displacement time histories are used to highlight variability among the selected solution procedures and model complexities. Finally, recommendations are made regarding the adequacy and robustness of each method, specifying the scenarios in which they are most effectively applied.
本研究探讨了大坝地震分析中与建模决策相关的可变性这一重要问题。传统上，结构建模和模拟采用渐进式方法，即逐步纳入更复杂的模型。例如，如果之前的水平表明地震安全系数不足，则会进行更高级的分析。认识到各种限制因素并评估各种方法的影响，对于提高大坝安全评估的理解力和有效性至关重要。为此，我们开展了一项广泛的参数研究，利用各种求解方法和模型复杂性来评估 Koyna 大坝和 Pine Flat 大坝的地震响应变异性。数值模拟在三个软件程序的二维框架内进行，包括不同的大坝系统配置。通过使用 Westergaard 添加的质量或声学元素模拟水库动态，引入了额外的复杂性。进行了线性和非线性分析，纳入了相关的材料特性，并在后者中采用了混凝土破坏塑性模型。利用模态参数和波峰位移时间历程来突出所选求解程序和模型复杂性之间的差异。最后，就每种方法的适当性和稳健性提出了建议，并具体说明了最有效应用这些方法的情况。
{"title":"Modeling Variability in Seismic Analysis of Concrete Gravity Dams: A Parametric Analysis of Koyna and Pine Flat Dams","authors":"B. Patra, Rocio L. Segura, Ashutosh Bagchi","doi":"10.3390/infrastructures9010010","DOIUrl":"https://doi.org/10.3390/infrastructures9010010","url":null,"abstract":"This study addresses the vital issue of the variability associated with modeling decisions in dam seismic analysis. Traditionally, structural modeling and simulations employ a progressive approach, where more complex models are gradually incorporated. For example, if previous levels indicate insufficient seismic safety margins, a more advanced analysis is then undertaken. Recognizing the constraints and evaluating the influence of various methods is essential for improving the comprehension and effectiveness of dam safety assessments. To this end, an extensive parametric study is carried out to evaluate the seismic response variability of the Koyna and Pine Flat dams using various solution approaches and model complexities. Numerical simulations are conducted in a 2D framework across three software programs, encompassing different dam system configurations. Additional complexity is introduced by simulating reservoir dynamics with Westergaard-added mass or acoustic elements. Linear and nonlinear analyses are performed, incorporating pertinent material properties, employing the concrete damage plasticity model in the latter. Modal parameters and crest displacement time histories are used to highlight variability among the selected solution procedures and model complexities. Finally, recommendations are made regarding the adequacy and robustness of each method, specifying the scenarios in which they are most effectively applied.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"40 11","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-03DOI: 10.3390/infrastructures9010009
Samiulhaq Wasiq, A. Golroo
Road networks play a significant role in each country’s economy, especially in countries such as Afghanistan, which is strategically located in the international transit path from Europe to East Asia. In such a country, pavement performance models are fundamental for the pavement maintenance planning that provides high-quality infrastructure for transporting goods and travelers. However, due to the lack of a budget for pavement monitoring and maintenance in Afghanistan, transportation networks and pavement condition data have not been widely acquired for the development of a pavement performance model. The main aim of this study is to use a machine learning technique to, for the first time, develop a pavement performance model for Afghanistan that uses simple, cost-effective, and fairly accurate data—collected via smartphones—and that is based on a case study of over 550 km of Afghanistan’s highways. First, the current condition of Afghanistan’s road network is investigated using a smartphone. Then, collected data are prepared and analyzed so as to estimate the pavement condition index (PCI). Finally, a pavement performance model for PCI is developed using pavement age with an adequate coefficient of determination of 0.70 and successfully validated. It is concluded that the proposed approach is efficient and effective when developing a performance model in other developing countries encountering such data and budget limitations.
{"title":"Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data","authors":"Samiulhaq Wasiq, A. Golroo","doi":"10.3390/infrastructures9010009","DOIUrl":"https://doi.org/10.3390/infrastructures9010009","url":null,"abstract":"Road networks play a significant role in each country’s economy, especially in countries such as Afghanistan, which is strategically located in the international transit path from Europe to East Asia. In such a country, pavement performance models are fundamental for the pavement maintenance planning that provides high-quality infrastructure for transporting goods and travelers. However, due to the lack of a budget for pavement monitoring and maintenance in Afghanistan, transportation networks and pavement condition data have not been widely acquired for the development of a pavement performance model. The main aim of this study is to use a machine learning technique to, for the first time, develop a pavement performance model for Afghanistan that uses simple, cost-effective, and fairly accurate data—collected via smartphones—and that is based on a case study of over 550 km of Afghanistan’s highways. First, the current condition of Afghanistan’s road network is investigated using a smartphone. Then, collected data are prepared and analyzed so as to estimate the pavement condition index (PCI). Finally, a pavement performance model for PCI is developed using pavement age with an adequate coefficient of determination of 0.70 and successfully validated. It is concluded that the proposed approach is efficient and effective when developing a performance model in other developing countries encountering such data and budget limitations.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"39 16","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-02DOI: 10.3390/infrastructures9010008
Ahmed Abouelsaad, Greg White, Ali Jamshidi
Asphalt mixtures age during service in the field, primarily as the result of chemical changes in the bituminous binder phase. The ageing phenomenon changes the properties of the asphalt mixture, including the stiffness modulus, the resistance to deformation and the resistance to cracking, and it leads to surface weathering or erosion that often leads to pavement resurfacing. Consequently, many researchers have attempted to understand and to simulate the ageing of bituminous binders and asphalt mixtures in the laboratory. This review of bituminous binder and asphalt mixture ageing considers ageing simulation techniques, the effect of ageing on both bituminous binders and asphalt mixtures, the potential benefits of ageing inhibitors, and efforts to relate simulated laboratory ageing to observed field ageing. It is concluded that ageing has a significant effect on the properties of bituminous binders and asphalt mixtures, and that improved simulated ageing is important for comparing the effect of ageing on different materials and mixtures, as well as for quantifying the potential benefits of ageing inhibitors, which have generally been promising. It is also concluded that current ageing protocols are based on heat only, omitting the important contribution of solar radiation to the weathering and ageing of asphalt surfaces in the field. In the future, different simulated ageing protocols should be developed for binder and mixture samples. Similarly, a different ageing protocol is appropriate for understanding base-layer fatigue, compared to research on surface-layer weathering. Finally, it is concluded that a universal ageing protocol is unlikely to be found and that mixture- and climate-specific protocols need to be developed. However, given the importance of simulated ageing to asphalt researchers, the development of reliable, robust and calibrated laboratory ageing protocols is essential for the future.
{"title":"State of the Art Review of Ageing of Bituminous Binders and Asphalt Mixtures: Ageing Simulation Techniques, Ageing Inhibitors and the Relationship between Simulated Ageing and Field Ageing","authors":"Ahmed Abouelsaad, Greg White, Ali Jamshidi","doi":"10.3390/infrastructures9010008","DOIUrl":"https://doi.org/10.3390/infrastructures9010008","url":null,"abstract":"Asphalt mixtures age during service in the field, primarily as the result of chemical changes in the bituminous binder phase. The ageing phenomenon changes the properties of the asphalt mixture, including the stiffness modulus, the resistance to deformation and the resistance to cracking, and it leads to surface weathering or erosion that often leads to pavement resurfacing. Consequently, many researchers have attempted to understand and to simulate the ageing of bituminous binders and asphalt mixtures in the laboratory. This review of bituminous binder and asphalt mixture ageing considers ageing simulation techniques, the effect of ageing on both bituminous binders and asphalt mixtures, the potential benefits of ageing inhibitors, and efforts to relate simulated laboratory ageing to observed field ageing. It is concluded that ageing has a significant effect on the properties of bituminous binders and asphalt mixtures, and that improved simulated ageing is important for comparing the effect of ageing on different materials and mixtures, as well as for quantifying the potential benefits of ageing inhibitors, which have generally been promising. It is also concluded that current ageing protocols are based on heat only, omitting the important contribution of solar radiation to the weathering and ageing of asphalt surfaces in the field. In the future, different simulated ageing protocols should be developed for binder and mixture samples. Similarly, a different ageing protocol is appropriate for understanding base-layer fatigue, compared to research on surface-layer weathering. Finally, it is concluded that a universal ageing protocol is unlikely to be found and that mixture- and climate-specific protocols need to be developed. However, given the importance of simulated ageing to asphalt researchers, the development of reliable, robust and calibrated laboratory ageing protocols is essential for the future.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"132 45","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-25DOI: 10.3390/infrastructures9010006
F. Catbas, Jacob Anthony Cano, Furkan Luleci, Lori C. Walters, Robert A. Michlowitz
This study investigates the capture of digital data and the development of models for structures with incomplete documentation and plans. LiDAR technology is utilized to obtain the point clouds of a pedestrian bridge structure. Two different point clouds with varying densities, (i) fine (11 collection locations) and (ii) coarse (4 collection locations), collected via terrestrial LiDAR, are analyzed to generate geometry and structural sections. This geometry is compared to the structural plans, which are then converted into numerical models (finite element—FE model) based on the point cloud data. Point cloud-based FE models (based on fine and coarse data) are compared with the structural plan-based FE model. It is observed that the static and dynamic responses are comparable within an acceptable range of a maximum difference of 5.5% for static deformation and an 8.23% frequency difference, with an average difference of less than 5%. Additionally, the dynamic properties of the fine and coarse point cloud FE models are compared with the operational modal analysis data obtained from the bridge. The fine and course point-cloud-based FE models, without model calibration, achieve an average accuracy of 8.76% and 9.94% for natural frequencies and a 0.89 modal assurance criterion value. The research found that the digital data generation yields promising results in this case for a bridge if documentation or plans are unavailable. With recent technologies and approaches such as digital twins, the connection between physical and virtual entities needs to be established by fusing digital models, sensorial information, and other data forms for better infrastructure management. Models such as those investigated and discussed in this paper can assist engineers with structural preservation in conjunction with monitoring data and utilization for digital twins.
本研究探讨了如何获取数字数据并为文件和图纸不完整的结构建立模型。利用激光雷达技术获取人行天桥结构的点云。通过地面激光雷达采集的两种不同密度的点云（i）精细点云（11 个采集点）和（ii）粗糙点云（4 个采集点）进行分析，以生成几何图形和结构剖面图。将几何图形与结构图进行比较，然后根据点云数据将结构图转换为数值模型（有限元-有限元模型）。基于点云的 FE 模型（基于精细和粗略数据）与基于结构平面的 FE 模型进行了比较。结果表明，静态和动态响应在可接受的范围内具有可比性，静态变形的最大差异为 5.5%，频率差异为 8.23%，平均差异小于 5%。此外，还将精细和粗糙点云 FE 模型的动态特性与从桥梁获得的运行模态分析数据进行了比较。在未进行模型校准的情况下，基于精细点云和粗糙点云的 FE 模型的固有频率平均精确度分别为 8.76% 和 9.94%，模态保证标准值为 0.89。研究发现，在这种情况下，如果没有文件或图纸，数字数据生成对桥梁来说会产生很好的效果。随着数字孪生等最新技术和方法的发展，需要通过融合数字模型、感知信息和其他数据形式来建立物理实体和虚拟实体之间的联系，从而更好地管理基础设施。本文所研究和讨论的模型可以帮助工程师结合监测数据和数字孪生的利用来进行结构保护。
{"title":"On the Generation of Digital Data and Models from Point Clouds: Application to a Pedestrian Bridge Structure","authors":"F. Catbas, Jacob Anthony Cano, Furkan Luleci, Lori C. Walters, Robert A. Michlowitz","doi":"10.3390/infrastructures9010006","DOIUrl":"https://doi.org/10.3390/infrastructures9010006","url":null,"abstract":"This study investigates the capture of digital data and the development of models for structures with incomplete documentation and plans. LiDAR technology is utilized to obtain the point clouds of a pedestrian bridge structure. Two different point clouds with varying densities, (i) fine (11 collection locations) and (ii) coarse (4 collection locations), collected via terrestrial LiDAR, are analyzed to generate geometry and structural sections. This geometry is compared to the structural plans, which are then converted into numerical models (finite element—FE model) based on the point cloud data. Point cloud-based FE models (based on fine and coarse data) are compared with the structural plan-based FE model. It is observed that the static and dynamic responses are comparable within an acceptable range of a maximum difference of 5.5% for static deformation and an 8.23% frequency difference, with an average difference of less than 5%. Additionally, the dynamic properties of the fine and coarse point cloud FE models are compared with the operational modal analysis data obtained from the bridge. The fine and course point-cloud-based FE models, without model calibration, achieve an average accuracy of 8.76% and 9.94% for natural frequencies and a 0.89 modal assurance criterion value. The research found that the digital data generation yields promising results in this case for a bridge if documentation or plans are unavailable. With recent technologies and approaches such as digital twins, the connection between physical and virtual entities needs to be established by fusing digital models, sensorial information, and other data forms for better infrastructure management. Models such as those investigated and discussed in this paper can assist engineers with structural preservation in conjunction with monitoring data and utilization for digital twins.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"20 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-25DOI: 10.3390/infrastructures9010005
Sipho G. Thango, G. Drosopoulos, S. M. Motsa, G. Stavroulakis
A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions.
{"title":"Prediction of the Response of Masonry Walls under Blast Loading Using Artificial Neural Networks","authors":"Sipho G. Thango, G. Drosopoulos, S. M. Motsa, G. Stavroulakis","doi":"10.3390/infrastructures9010005","DOIUrl":"https://doi.org/10.3390/infrastructures9010005","url":null,"abstract":"A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"36 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-22DOI: 10.3390/infrastructures9010004
M. Pepe, D. Costantino, V. Alfio
The aim of the paper is to identify a suitable method for assessing the deformation of structures (buildings, bridges, walls, etc.) by means of topographic measurements of significant targets positioned on the infrastructure under consideration. In particular, the paper describes an approach to testing a bridge in a mixed structure (concrete and steel). The methodological approach developed can be schematised into the following main phases: (i) surveying using total stations (TSs) in order to obtain the spatial coordinates of the targets by means of the three-dimensional intersection technique (planimetric and altimetric measurements); (ii) least-squares compensation for the measurements performed; (iii) displacement analysis; and (iv) statistical evaluation of the reliability of the results. This method was evaluated on a case study of a newly built double-track railway bridge, located near the metropolitan area of the city of Bari, Italy, during various loading and unloading activities. The results obtained, evaluated by means of certain statistical tests, made it possible to verify the structural suitability of the bridge.
{"title":"Topographic Measurements and Statistical Analysis in Static Load Testing of Railway Bridge Piers","authors":"M. Pepe, D. Costantino, V. Alfio","doi":"10.3390/infrastructures9010004","DOIUrl":"https://doi.org/10.3390/infrastructures9010004","url":null,"abstract":"The aim of the paper is to identify a suitable method for assessing the deformation of structures (buildings, bridges, walls, etc.) by means of topographic measurements of significant targets positioned on the infrastructure under consideration. In particular, the paper describes an approach to testing a bridge in a mixed structure (concrete and steel). The methodological approach developed can be schematised into the following main phases: (i) surveying using total stations (TSs) in order to obtain the spatial coordinates of the targets by means of the three-dimensional intersection technique (planimetric and altimetric measurements); (ii) least-squares compensation for the measurements performed; (iii) displacement analysis; and (iv) statistical evaluation of the reliability of the results. This method was evaluated on a case study of a newly built double-track railway bridge, located near the metropolitan area of the city of Bari, Italy, during various loading and unloading activities. The results obtained, evaluated by means of certain statistical tests, made it possible to verify the structural suitability of the bridge.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"8 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-20DOI: 10.3390/infrastructures9010003
Zahra Ameli, Shabnam Jafarpoor Nesheli, Eric N. Landis
The application of deep learning (DL) algorithms has become of great interest in recent years due to their superior performance in structural damage identification, including the detection of corrosion. There has been growing interest in the application of convolutional neural networks (CNNs) for corrosion detection and classification. However, current approaches primarily involve detecting corrosion within bounding boxes, lacking the segmentation of corrosion with irregular boundary shapes. As a result, it becomes challenging to quantify corrosion areas and severity, which is crucial for engineers to rate the condition of structural elements and assess the performance of infrastructures. Furthermore, training an efficient deep learning model requires a large number of corrosion images and the manual labeling of every single image. This process can be tedious and labor-intensive. In this project, an open-source steel bridge corrosion dataset along with corresponding annotations was generated. This database contains 514 images with various corrosion severity levels, gathered from a variety of steel bridges. A pixel-level annotation was performed according to the Bridge Inspectors Reference Manual (BIRM) and the American Association of State Highway and Transportation Officials (AASHTO) regulations for corrosion condition rating (defect #1000). Two state-of-the-art semantic segmentation algorithms, Mask RCNN and YOLOv8, were trained and validated on the dataset. These trained models were then tested on a set of test images and the results were compared. The trained Mask RCNN and YOLOv8 models demonstrated satisfactory performance in segmenting and rating corrosion, making them suitable for practical applications.
近年来，深度学习（DL）算法因其在结构损伤识别（包括腐蚀检测）方面的卓越性能而备受关注。人们对卷积神经网络（CNN）在腐蚀检测和分类中的应用越来越感兴趣。然而，目前的方法主要涉及在边界框内检测腐蚀，缺乏对边界形状不规则的腐蚀进行分割。因此，量化腐蚀区域和严重程度变得非常具有挑战性，而这对于工程师评定结构元素的状况和评估基础设施的性能至关重要。此外，训练一个高效的深度学习模型需要大量的腐蚀图像和对每张图像进行手动标注。这一过程既繁琐又耗费人力。在本项目中，生成了一个开源钢桥腐蚀数据集和相应的注释。该数据库包含 514 张不同腐蚀严重程度的图像，这些图像来自各种钢桥。根据《桥梁检查员参考手册》（Bridge Inspectors Reference Manual，BIRM）和美国州公路与运输官员协会（American Association of State Highway and Transportation Officials，AASHTO）关于腐蚀状况评级（1000 号缺陷）的规定，进行了像素级注释。两种最先进的语义分割算法 Mask RCNN 和 YOLOv8 在数据集上进行了训练和验证。然后在一组测试图像上测试了这些训练有素的模型，并对结果进行了比较。经过训练的 Mask RCNN 和 YOLOv8 模型在分割和评级腐蚀方面表现令人满意，适合实际应用。
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Pub Date : 2023-12-20DOI: 10.3390/infrastructures9010002
Marcin Michalak, J. Bagiński, Andrzej Białas, Artur Kozłowski, Marek Sikora
This paper presents a generic component for Analytic Hierarchy Process (AHP)-based decision support in risk management. The component was originally dedicated to railway transportation issues; however, its generality enabled it to extend its functionality for other domains too. To show the generality of the module and possibility of its application in other domains, an environmental case was run. Its goal was to select methods for planning the post-mining heap revitalization process, especially decision-making focusing on the selection of the most advantageous revitalization option on the basis of the Analytic Hierarchy Process and different, non-financial factors, e.g., social, environmental, technological, political, etc. Taking into account expert responses, the suggested solution was related to energy production.
{"title":"A Generic Component for Analytic Hierarchy Process-Based Decision Support and Its Application for Postindustrial Area Management","authors":"Marcin Michalak, J. Bagiński, Andrzej Białas, Artur Kozłowski, Marek Sikora","doi":"10.3390/infrastructures9010002","DOIUrl":"https://doi.org/10.3390/infrastructures9010002","url":null,"abstract":"This paper presents a generic component for Analytic Hierarchy Process (AHP)-based decision support in risk management. The component was originally dedicated to railway transportation issues; however, its generality enabled it to extend its functionality for other domains too. To show the generality of the module and possibility of its application in other domains, an environmental case was run. Its goal was to select methods for planning the post-mining heap revitalization process, especially decision-making focusing on the selection of the most advantageous revitalization option on the basis of the Analytic Hierarchy Process and different, non-financial factors, e.g., social, environmental, technological, political, etc. Taking into account expert responses, the suggested solution was related to energy production.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":"101 16","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138958801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.3390/infrastructures9010001
Shayan Jorjam, Mohammed Mawlana, Amin Hammad
The traditional method of installing underground utilities (e.g., water, sewer, gas pipes, electrical cables) by burying them under roads has been used for decades. However, the repeated excavations related to this method cause problems, such as traffic congestion and business disruption, which can significantly increase financial and social costs. Multi-purpose Utility Tunnels (MUTs) are a good alternative for buried utilities. Although the initial cost of MUTs is higher than that of the traditional method, social cost savings make them more feasible, especially in dense urban areas. Different factors, such as the specifications of utilities, the location of the MUTs, and the construction method, should be investigated to determine if MUTs can be an economical and practical alternative. The construction method is one of the most important factors to assess to have a successful MUT project and reduce its impact on the surrounding area. Simulation can be used to investigate the different construction methods of MUTs. In this paper, two Stochastic Discrete Event Simulation models depicting two MUT construction methods (i.e., microtunneling and cut-and-cover) are developed to analyze the duration and cost of the MUT projects. Also, 4D simulation models of these methods are developed for constructability assessment of these projects.
{"title":"Stochastic Simulation of Construction Methods for Multi-purpose Utility Tunnels","authors":"Shayan Jorjam, Mohammed Mawlana, Amin Hammad","doi":"10.3390/infrastructures9010001","DOIUrl":"https://doi.org/10.3390/infrastructures9010001","url":null,"abstract":"The traditional method of installing underground utilities (e.g., water, sewer, gas pipes, electrical cables) by burying them under roads has been used for decades. However, the repeated excavations related to this method cause problems, such as traffic congestion and business disruption, which can significantly increase financial and social costs. Multi-purpose Utility Tunnels (MUTs) are a good alternative for buried utilities. Although the initial cost of MUTs is higher than that of the traditional method, social cost savings make them more feasible, especially in dense urban areas. Different factors, such as the specifications of utilities, the location of the MUTs, and the construction method, should be investigated to determine if MUTs can be an economical and practical alternative. The construction method is one of the most important factors to assess to have a successful MUT project and reduce its impact on the surrounding area. Simulation can be used to investigate the different construction methods of MUTs. In this paper, two Stochastic Discrete Event Simulation models depicting two MUT construction methods (i.e., microtunneling and cut-and-cover) are developed to analyze the duration and cost of the MUT projects. Also, 4D simulation models of these methods are developed for constructability assessment of these projects.","PeriodicalId":13601,"journal":{"name":"Infrastructures","volume":" 497","pages":""},"PeriodicalIF":2.6,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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