Predicting bridge elements deterioration, using Collaborative Gaussian Process Regression

Abstract: Roadway and railway bridges are not only integral, but also vulnerable parts of terrestrial transport networks. Structural failures of bridges may lead to disastrous consequences on users and society at large. Bridge predictive deterioration models are extremely important for effective maintenance decision-making. However, the lack of enough inspection data between maintenance activities of a bridge complicates the development of accurate predictive models. Presented herein is a Gaussian Process Regression (GPR) based collaborative model for predicting the condition of bridge elements with limited available inspection data per bridge. This model has been applied in 137 bridge decks, showing that collaborative prognosis has the potential to predict the condition of different types of bridge elements, composing different types of bridges.This work was supported by the European Community’s H2020 Programme MG7-1-2017 Resilience to extreme (natural and man- made) events [grant number 769255] - “GIS-based infrastructure management system for optimised response to extreme events of terrestrial transport networks (SAFEWAY)

Predictive Group Maintenance Model for Networks of Bridges

Recent progress in the monitoring and prediction of the condition of infrastructure using sensing technologies has motivated researchers and infrastructure owners to explore the benefits of asset predictive maintenance, as an alternative to reactive maintenance. However, the application of predictive group maintenance for multi-system multi-component networks (MSMCN) has not received much attention in the literature or in practice. The paper presents an approach that prioritizes the maintenance of MSMCN of bridges, using a deterioration model of components with uncertainty, a lifecycle cost model, a predictive model for the optimal time for maintenance based on the latest inspection, a group maintenance model to reduce setup cost, and a scheduling model considering budget constraints. This model has been applied to a network of 15 bridges constituted by multiple heterogeneous components, and, compared with the Structures Investment Toolkit, it showed potential for a substantial decrease in maintenance costs, thus highlighting the practical significance of the presented approach. EU H202

Cloaking nanoparticles with protein corona shield for targeted drug delivery

Targeted drug delivery using nanoparticles can minimize the side effects of conventional pharmaceutical agents and enhance their efficacy. However, translating nanoparticle-based agents into clinical applications still remains a challenge due to the difficulty in regulating interactions on the interfaces between nanoparticles and biological systems. Here, we present a targeting strategy for nanoparticles incorporated with a supramolecularly pre-coated recombinant fusion protein in which HER2-binding affibody combines with glutathione-S-transferase. Once thermodynamically stabilized in preferred orientations on the nanoparticles, the adsorbed fusion proteins as a corona minimize interactions with serum proteins to prevent the clearance of nanoparticles by macrophages, while ensuring systematic targeting functions in vitro and in vivo. This study provides insight into the use of the supramolecularly built protein corona shield as a targeting agent through regulating the interfaces between nanoparticles and biological systems

Patch defects detection for pavement assessment, using smartphones and support vector machines

The condition evaluation of roadway transport networks is conducted to provide decision support for appropriate maintenance activities, preventing the possibility of detrimental effects. The costly, time-consuming and subjective current pavement assessment methods lead to the requirement for automation of the underlying process. Presented herein is an automated methodology for pavement patches detection; a process which is crucial for pavement surface evaluation and rating. Support Vector Machine (SVM) Classification is utilized, whilst the possibility of collecting pavement frames from smartphones, positioned insides of cars is examined. The SVM is trained and tested by feature vectors generated from the histogram and two texture descriptors of non-overlapped square blocks, which constitute an image. The outcome is the indication of the frames that include patches and the image blocks which are characterized as parts of patches

Vision- and Entropy-Based Detection of Distressed Areas for Integrated Pavement Condition Assessment

Pavement management systems aim to secure roadways functionality and vehicle passengers' safety by proposing strategies for pavement assessment and maintenance. However, transportation departments lack accurate, low-cost, and efficient methods for pavement assessment. Presented in this paper is a vision-based system for the detection of distressed pavement areas using low-cost technologies. Videos of pavement surface are recorded by a camera placed at the rear of a passenger vehicle, moving in a real-life urban network under normal traffic conditions. Collected data is processed by a developed algorithm that identifies video frames, including any type of pavement defect, using image entropy with a frame-based classification accuracy, precision, recall, and F1 score of 89.2%, 86.6%, 85.6%, and 86.1%, respectively. The proposed system can serve as the basis of any integrated pavement management system, saving significant amounts of time and cost for transportation departments

Automated patch detection and quantification for pavement evaluation

Pavement evaluation is a critical practice for the maintenance of roadway transport networks. Nevertheless, the current methods for pavement defects detection and assessment are subjective, costly and time-consuming; creating the need for automation of the underlying processes and for the use of low-cost technologies. In this paper, an automated system is presented for the identification and quantification of pavement patches; a crucial part of pavement surface assessment and rating, which depend on the proportion of road segments covered by patches. The proposed vision-based algorithm uses road surface frames collected by a camera, located on a vehicle moving in a real-life urban network. A Support Vector Machine is trained and tested by feature vectors, generated from the histogram and two texture descriptors of non-overlapped square blocks, which are located in ‘‘patch’’ and ‘‘no-patch’’ areas of the collected images. The outcome is composed of block-based categorization, image-based classification, and measurement of the patch area

Automated detection of pavement patches utilizing support vector machine classification

The efficient condition assessment of road networks is crucial to prevent pavement distresses which can cause a spectrum of detrimental effects. The need for automation of the underlying process is originated from the costly, time-consuming and dangerous current methods. Presented herein is the automation of the patch detection process, which is essential for pavement surface evaluation and rating. The method is based on Support Vector Machine (SVM) Classification. The road pavement images are divided into square blocks and the SVM is trained and tested by feature vectors generated from these blocks. The feature vectors consist of the histogram and two texture descriptors, using the discrete cosine transform (DCT) and the Gray-Level Co-Occurrence Matrix (GLCM). The output is a binary image, where each image block is classified as patch or no-patch. The performance of the proposed MatlabTM implementation, which uses data collected from real-life urban networks, is rated by a detection accuracy of 81.97 %, a precision of 64.21 %, and a recall of 91.21 %

Predicting bridge elements deterioration, using Collaborative Gaussian Process Regression

Roadway and railway bridges are not only integral, but also vulnerable parts of terrestrial transport networks. Structural failures of bridges may lead to disastrous consequences on users and society at large. Bridge predictive deterioration models are extremely important for effective maintenance decision-making. However, the lack of enough inspection data between maintenance activities of a bridge complicates the development of accurate predictive models. Presented herein is a Gaussian Process Regression (GPR) based collaborative model for predicting the condition of bridge elements with limited available inspection data per bridge. This model has been applied in 137 bridge decks, showing that collaborative prognosis has the potential to predict the condition of different types of bridge elements, composing different types of bridges

Pavement defects detection and classification using smartphone-based vibration and video signals

Presented herein is a big-data driven methodology for the detection of roadway anomalies, utilizing smartphone-based data and image signal streams. The methodology uses a vibration-based method and artificial intelligence for the detection of vibration-inducing anomalies, and a vision-based method with entropic texture segmentation filters and support vector machine (SVM) classification for the detection of patch defects on roadway pavements. The presented system pre-processes video streams for the identification of video frames of changes in image-entropy values, isolates these frames and performs texture segmentation to identify pixel areas of significant changes in entropy values, and then classifies and quantifies these areas using SVMs. The developed SVM is trained and tested by feature vectors generated from the image histogram and two texture descriptors of non-overlapped square blocks, which constitute images that includes ‘‘patch’’ and ‘‘no-patch’’ areas. The outcome is composed of block-based and image-based classification, as well as of measurements of the patch area

Automated Pavement Patch Detection and Quantification Using Support Vector Machines

Pavement condition evaluation provides transportation authorities with decision support tools for the selection of appropriate repair or replace actions, thus preventing the possibility of transportation networks disruption. The current costly, time-consuming, and subjective pavement assessment tactics require automation, combined with the application of low-cost technologies for more widespread deployment. Presented herein is an automated vision-based method for detecting and quantifying pavement patches; a critical aspect of pavement surface valuation and rating. The proposed system uses road surface video frames acquired either by a smartphone or an external camera, positioned respectively inside and outside of a moving passenger vehicle. Support vector machine classification applied to feature vectors, generated from the image and defined by two texture descriptors plus the histogram of nonoverlapped square blocks, characterized image blocks as parts patch or no-patch areas. The output consists of block-based and image-based classifications, while applications of the method to test video frames demonstrates a detection accuracy of 87.3 and 82.5% respectively. Additionally, the patch area is quantified with a percent absolute error of 11.04%