Seismic Risk Analysis of Revenue Losses, Gross Regional Product and transportation systems.

Natural threats like earthquakes, hurricanes or tsunamis have shown seri- ous impacts on communities. In the past, major earthquakes in the United States like Loma Prieta 1989, Northridge 1994, or recent events in Italy like L’Aquila 2009 or Emilia 2012 earthquake emphasized the importance of pre- paredness and awareness to reduce social impacts. Earthquakes impacted businesses and dramatically reduced the gross regional product. Seismic Hazard is traditionally assessed using Probabilistic Seismic Hazard Anal- ysis (PSHA). PSHA well represents the hazard at a specific location, but it’s unsatisfactory for spatially distributed systems. Scenario earthquakes overcome the problem representing the actual distribution of shaking over a spatially distributed system. The performance of distributed productive systems during the recovery process needs to be explored. Scenario earthquakes have been used to assess the risk in bridge networks and the social losses in terms of gross regional product reduction. The proposed method for scenario earthquakes has been applied to a real case study: Treviso, a city in the North East of Italy. The proposed method for scenario earthquakes requires three models: one representation of the sources (Italian Seismogenic Zonation 9), one attenuation relationship (Sa- betta and Pugliese 1996) and a model of the occurrence rate of magnitudes (Gutenberg Richter). A methodology has been proposed to reduce thou- sands of scenarios to a subset consistent with the hazard at each location. Earthquake scenarios, along with Mote Carlo method, have been used to simulate business damage. The response of business facilities to earthquake has been obtained from fragility curves for precast industrial building. Fur- thermore, from business damage the reduction of productivity has been simulated using economic data from the National statistical service and a proposed piecewise “loss of functionality model”. To simulate the economic process in the time domain, an innovative businesses recovery function has been proposed. The proposed method has been applied to generate scenarios earthquakes at the location of bridges and business areas. The proposed selection method- ology has been applied to reduce 8000 scenarios to a subset of 60. Subse- quently, these scenario earthquakes have been used to calculate three system performance parameters: the risk in transportation networks, the risk in terms of business damage and the losses of gross regional product. A novel model for business recovery process has been tested. The proposed model has been used to represent the business recovery process and simulate the effects of government aids allocated for reconstruction. The proposed method has efficiently modeled the seismic hazard using scenario earthquakes. The scenario earthquakes presented have been used to assess possible consequences of earthquakes in seismic prone zones and to increase the preparedness. Scenario earthquakes have been used to sim- ulate the effects to economy of the impacted area; a significant Gross Regional Product reduction has been shown, up to 77% with an earthquake with 0.0003 probability of occurrence. The results showed that limited funds available after the disaster can be distributed in a more efficient way

1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability

Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning

New Model for Bridge Management System (BMS): Bridge Repair Priority Ranking System (BRPRS), Case Based Reasoning for Bridge Deterioration, Cost Optimization, and Preservation Strategy

Most public transportation agencies (Such as, state department of transportations (DOTs) and department of public works for cities and towns.) in the United States are constantly pursuing ways to improve bridge asset management to optimize their use of limited available funds for rehabilitation, replacement, and preventive maintenance. Given the realities of available funding, there is a significant difference between available funds and funds required for maintaining bridges in good condition. The proper preventative maintenance and treatments should be performed at the right time to be cost effective and extend the life of bridges. Neglecting maintenance can cause higher future costs and further deteriorate the conditions that will increase the risk of bridge closure. This would require complete or partial replacement as well as additional funds needed for detours and traffic control which interrupts services to the motorist and creates more congestion. Development and implementation of a Bridge Management System (BMS) provide states and municipalities with a tool to help identify maintenance repair, prioritize bridge rehabilitation and replacement, develop preservation strategies, and allocate available funds accordingly. The primary objective of this research is to develop a Bridge Management System (BMS) to manage municipal and state bridge assets. Complete, accurate data in well-designed form is vital to a Bridge Management System (BMS). This system will make available work reports, engineering drawings, photographs, and a forecasting model for management staff use. Inventory and condition data are extracted from the U.S. Federal Highway Administration (FHWA) and National Bridge Inventory System (NBIS) coding guidelines. The proposed model provides: (1) A priority ranking system for Rehabilitation and Replacement projects, which enables the decision-makers to understand and compare the overall state of all the bridges in the network. It embraces seven factors condition, criticality, risk, functionally, bridge type, age, and size. (2) A deterioration model that uses optimized case-based reasoning (CBR) method. A similarity measure of classification is developed to identify how close the characteristics of bridge components are to each other based on a scoring system. (3) A cost model that considers different repair strategies and provide bridge repair recommendations with estimated cost repairs. (4)The model feeds data to a forecasting program that prepares 120-year preservation, maintenance, repair and rehabilitation budgets and schedules to sustain a bridge network at the highest performance level under approved budgets. The forecasting option contains default management costs that are upgraded as work report data yields costs based on locality and individual bridge projects. BMS will give accessibility through linkages to all available municipal, and DOT, bridge data in the state. The data will be available through ArcGIS on tablets, laptops, and smartphones with access to cloud storage

Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges

As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to author

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

[EN] With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.This research is supported by the Branch of China Road and Bridge Corporation (Cambodia) Technology Development Project(No.2020-zlkj-04); National Social Science Fund projects (No.20BJY010); National Social Science Fund Post-financing projects (No.19FJYB017); Sichuan-tibet Railway Major Fundamental Science Problems Special Fund (No.71942006); Qinghai Natural Science Foundation (No.2020-JY-736); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (No.2018-GH-006 and No.2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No.E-GKRWJC20202914); Shaanxi Social Science Fund (No.2017S004); Xi'an Construction Science and Technology Planning Project (No.SZJJ201915 and No.SZJJ201916); Shaanxi Province Higher Education Teaching Reform Project (No.19BZ016); Fundamental Research for Funds for the Central Universities (Humanities and Social Sciences), Chang'an University (No.300102239616, No.300102281669 and No.300102231641).Ma, Z.; Zhang, J.; Philbin, SP.; Li, H.; Yang, J.; Feng, Y.; Ballesteros-Pérez, P.... (2021). Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement. Buildings. 11(12):1-18. https://doi.org/10.3390/buildings11120577S118111

Dynamic Quality Monitoring System to Assess the Quality of Asphalt Concrete Pavement

With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site