Journal of infrastructure preservation and resilience最新文献

{"title":"Automated crack identification in structures using acoustic waveforms and deep learning","authors":"Mohamed Barbosh, Liangfu Ge, Ayan Sadhu","doi":"10.1186/s43065-024-00102-2","DOIUrl":"https://doi.org/10.1186/s43065-024-00102-2","url":null,"abstract":"Structural elements undergo multiple levels of damage at various locations due to environments and critical loading conditions. The level of damage and its location can be predicted using acoustic emission (AE) waveforms that are captured from the generation of inherent microcracks. Existing AE methods are reliant on the feature selection of the captured waveforms and may be subjective in nature. To automate this process, this paper proposes a deep-learning model to predict the damage severity and its expected location using AE waveforms. The model is based on a densely connected convolutional neural network (CNN) that offers superior feature extraction and minimal training data requirements. Time-domain AE waveforms are used as inputs of the proposed model to automate the process of predicting the severity of damage and identifying the expected location of the damage in structural elements. The proposed approach is validated using AE data collected from a concrete beam and a wooden beam and plate. The results show the capability of the proposed method for predicting the level of damage with an accuracy range of 92-95% and identifying the approximate location of damage with 90-100% accuracy. Thus, the proposed method serves as a robust technique for damage severity prediction and localization in civil structures.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948059","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}

{"title":"Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms","authors":"Karthikeyan Loganathan, Mohammad Najafi, Sharareh Kermanshachi, Praveen Kumar Maduri, Apurva Pamidimukkala","doi":"10.1186/s43065-024-00101-3","DOIUrl":"https://doi.org/10.1186/s43065-024-00101-3","url":null,"abstract":"Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870881","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}

{"title":"Numerical investigation on the deformation of railway embankment under normal faulting","authors":"Haohua Chen, Jiankun Liu, Zhijian Li, Xiaoqiang Liu, Jiyun Nan, Jingyu Liu","doi":"10.1186/s43065-024-00100-4","DOIUrl":"https://doi.org/10.1186/s43065-024-00100-4","url":null,"abstract":"","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645168","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}

{"title":"Evaluation of the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to ultrasonic measurements","authors":"Pierre-Philippe Beaujean, Samuel R. Shaffer, Francisco Presuel-Moreno, Matthew Brogden","doi":"10.1186/s43065-024-00099-8","DOIUrl":"https://doi.org/10.1186/s43065-024-00099-8","url":null,"abstract":"The use of reinforced concrete is foundational to modern infrastructure. Acknowledging this, it is imperative that health monitoring techniques be in place to study corrosion within these structures. By using a non-destructive method for detecting the early formation of cracks within reinforced concrete, the method presented in this paper seeks to improve upon traditional techniques of monitoring corrosion, within reinforced concrete structures. In this paper, the authors present a method to evaluate the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to a set of ultrasonic measurements, which constitutes a novel approach to the problem of observing the impact of corroding rebars and resulting concrete damage. A non-contact ultrasonic transducer is operated at a carrier frequency of 500 [kHz], with a layer of saltwater separating the sensor from the concrete surface. Following this non-contact measurement collection of the surface and rebar echo responses, a poro-elastic model is used to model the sound propagation, through an adapted version of the Biot-Stoll model. At first, a set of default parameters, obtained from the physical characteristics of the reinforced concrete, are used to match experimental and simulated acoustic signature of the sample. Performing statistical averaging along the corroding rebar within three samples over a period of nearly nine months, a small but monotonous increase in the distance between the concrete surface and the top of the rebar, indicating gradual corrosion of the rebar. Next, a non-linear optimization algorithm is used to optimize the match between measured and simulated echoes. Through the implementation of this model parameter optimization, the root mean square error between measured and simulated responses was reduced by 63.7% for the full signal, and 62.6% for the rebar echo.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188487","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}

{"title":"An investigation of belief-free DRL and MCTS for inspection and maintenance planning","authors":"Daniel Koutas, Elizabeth Bismut, Daniel Straub","doi":"10.1186/s43065-024-00098-9","DOIUrl":"https://doi.org/10.1186/s43065-024-00098-9","url":null,"abstract":"We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I &M) planning. Unlike other DRL algorithms for (I &M) planning, the proposed +RQN architecture dispenses with computing the belief state and directly handles erroneous observations instead. We apply the algorithm to a basic I &M planning problem for a one-component system subject to deterioration. In addition, we investigate the performance of Monte Carlo tree search for the I &M problem and compare it to the +RQN. The comparison includes a statistical analysis of the two methods’ resulting policies, as well as their visualization in the belief space.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140810081","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}

{"title":"Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons","authors":"Mohamed T. Elshazli, Dina Hussein, Ganapati Bhat, Ahmed Abdel-Rahim, Ahmed Ibrahim","doi":"10.1186/s43065-024-00096-x","DOIUrl":"https://doi.org/10.1186/s43065-024-00096-x","url":null,"abstract":"The operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, transportation safety, and the sustainability of infrastructure. Truck platooning is an emerging truck configuration that is expected to become operational in the future due to the rapid advancements in connected vehicle technology and autonomous driving assistance. The platooning configuration enables trucks to be connected with themselves and the surrounding infrastructure. This arrangement has shown to be a promising solution to improve the vehicles’ fuel efficiency, reduce carbon dioxide emission, reduce traffic congestion, and improve transportation service. However, platooning may accelerate the damage accumulation of pavement and bridge structures due to the formation of multiple load axles within each platoon since those structures were not designed for such loads. According to AASHTO, bridges are designed based on a notional live load model comprised of one or two trucks per lane in conjunction with or separate from an applied uniform load (AASHTO, LRFD 2022). This damage, if accumulated, its repair would require billions of dollars from the government and would impede the movement of both people and goods. The potential damage to infrastructure may arise due to various factors such as the number of trucks in a platoon, gap spacing between trucks, and the type of trucks. This research work includes a thorough parametric study with 295,200 computer simulations using SAP 2000. The goal was to evaluate the effect of different truck platooning configurations on the load rating of existing bridges. The obtained results served as the dataset for training various machine learning models, including Random Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). Results showed that Random Forest model performed the best, with the lowest prediction errors. The proposed machine learning model has shown its effectiveness in identifying optimal platooning configurations for bridge structures within the scope of the study. ","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603454","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}

{"title":"Resilience and systems- A traffic flow case example","authors":"Khalilullah Mayar, David G. Carmichael, Xuesong Shen","doi":"10.1186/s43065-024-00097-w","DOIUrl":"https://doi.org/10.1186/s43065-024-00097-w","url":null,"abstract":"Resilience has increasingly become a crucial topic to the function of various real-world systems as our planet undergoes a rising trend of uncertainty and change due to natural, human and technological causes. Despite its ubiquitous use, the term resilience is poorly and often inconsistently used in various disciplines, hindering its universal understanding and application. This study applies the resilience system interpretation framework, which defines resilience irrespective of its disciplinary association, in the form of adaptation and adaptive systems, to two traffic flow systems. The system framework defines resilience as the ability of the system state and form to return to their initial or other suitable state or form through passive and active feedback structures. Both components of the system framework are demonstrated through practical simulation scenarios on the modified viscous Burgers’ equation and the LWR-Greenshields model equipped with an adaptive Extremum seeking control, respectively. This novel and systematic understanding of resilience will advance resilience analysis, design, and measurement processes in various real-world systems in a unified fashion and subsequently pave the way for resilience operationalization and its integration into industry standards. A novel system definition for resilience and its constituent elements in the form of adaption is presented. The system framework is subsequently applied to two simple traffic flow systems. Modified viscous Burgers’ equation and LWR-Greenshields model equipped with an adaptive Extremum seeking control demonstrate the passive and active feedback structures as the two tools for obtaining system resilience. This cross-disciplinary system framework offers the potential for a greater understanding of resilience, eliminates overlap, and paves the way toward resilience operationalization.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313796","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}

{"title":"Modeling retroreflectivity degradation of pavement markings across the US with advanced machine learning algorithms","authors":"Ipshit Ibne Idris, Momen Mousa, Marwa Hassan","doi":"10.1186/s43065-024-00094-z","DOIUrl":"https://doi.org/10.1186/s43065-024-00094-z","url":null,"abstract":"Retroreflectivity is the primary metric that controls the visibility of pavement markings during nighttime and in adverse weather conditions. Maintaining the minimum level of retroreflectivity as specified by Federal Highway Administration (FHWA) is crucial to ensure safety for motorists. The key objective of this study was to develop robust retroreflectivity prediction models that can be used by transportation agencies to reliably predict the retroreflectivity of their pavement markings utilizing the initially measured retroreflectivity and other key project conditions. A total of 49,632 transverse skip retroreflectivity measurements of seven types of marking materials were retrieved from the eight most recent test decks covered under the National Transportation Product Evaluation Program (NTPEP). Decision Tree (DT) and Artificial Neural Network (ANN) algorithms were considered for developing performance prediction models to estimate retroreflectivity at different prediction horizons for up to three years. The models were trained with randomly selected 80% data points and tested with the remaining 20% data points. Sequential ANN models exhibited better performance with the testing data than the sequential DT models. The training and testing R2 ranges of the sequential ANN models were from 0.76 to 0.96 and 0.55 to 0.94, respectively, which were significantly higher than the R2 range (0.14 to 0.75) from the regression models proposed in past studies. Initial or predicted retroreflectivity, snowfall, and traffic were found to be the most important inputs to model predictions.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139921640","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}

{"title":"Surface crack treatment of concrete via nano-modified microbial carbonate precipitation","authors":"Tao Li, Hanqing Yang, Xiaohui Yan, Maolin He, Haojie Gu, Liming Yu","doi":"10.1186/s43065-024-00095-y","DOIUrl":"https://doi.org/10.1186/s43065-024-00095-y","url":null,"abstract":"As a new concrete crack patching technology, microbial self-healing slurries offer favourable characteristics including non-pollution, ecological sustainability and good compatibility with concrete. In this paper, a nano-sio2-modified microbial bacteria liquid, combined with sodium alginate and polyvinyl alcohol, was used to prepare a nano-modified microbial self-healing slurry. This slurry was used to coat concrete under negative pressure in order to verify its restoration effect, and the micromorphology of the resulting microbial mineralization products was observed. The results revealed that patching the concrete using the nano-modified microbial slurry significantly improved its permeability, and increased its carbonization resistance by three times in comparison with the control group. Through a combination of Scanning electron microscopy (SEM) and X-ray diffraction (XRD) observation, it was determined that the microbial mineralization reaction products were mainly calcite crystals, which, integrated with the nano-sio2, sodium alginate and polyvinyl alcohol at the microscopic level, filled the internal pores of concrete, thus improving its durability. • Surface crack treatment of concrete using a nano-modified microbial slurry was investigated. • Patching concrete using nano-microbial slurry clearly improved its chloride penetration. • The carbonization of the concrete was three times in comparision with the control group. • The main product of the microbial mineralization reaction was calcite crystal.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139911126","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}

{"title":"The mechanism of spontaneous corrugation on the snowy and icy roads produced by the moving vehicles in cold regions","authors":"Hao Zheng, Yu Cao, Chongqian Ma, Shunji Kanie","doi":"10.1186/s43065-024-00093-0","DOIUrl":"https://doi.org/10.1186/s43065-024-00093-0","url":null,"abstract":"Traffic safety in cold regions is seriously affected by the snow and ice brought by the extreme climate. The snowy and icy road cannot provide enough friction for the safe operation of vehicles due to its smooth and uneven surface. In this research, we are going to focus on the uneven corrugation occurred on snowy and icy roads and to investigate the formation mechanism of this spontaneous corrugation which can seriously threaten the traffic safety. According to field observations, we found that the corrugation phenomenon generated by moving vehicles is a complicated thermal–mechanical coupled process. In order to simplify this complicated process, we are going to focus on the mechanical process of the formation of spontaneous corrugation only at this stage. Field observation by time-lapse cameras has been conducted to disclose its forming process directly. Then, we adopted sand as the material to reproduce the spontaneous corrugation in the laboratory which can eliminate the influence of the thermal process. By considering the compressibility and mobility of the surface material comprehensively, a numerical model has been successfully constructed for imitating the forming process of corrugation. Then based on this proposed numerical model, a preliminary discussion on the influence of natural frequency on the number of the corrugation has been conducted. The relationship between the natural frequency which is decided by the vehicle itself and the corrugation is promising to be utilized in optimizing the vehicle design to improve the performance on the snowy and icy roads.","PeriodicalId":73793,"journal":{"name":"Journal of infrastructure preservation and resilience","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139588043","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}