Methodologies for the assessment of industrial and energy assets, based on data analysis and BI

In July 2020, post pandemic onset, Europe launched the Next Generation EU (NGEU) program. The amount of resources deployed to revitalize Europe has reached 750 billion. The NGEU initiative directs significant resources to Italy. These funds can enable our country to boost investment and increase employment. The missions of Italian Recovery and Resilience Plan (PNRR) include digitization, innovation and sustainable mobility (rail network investments, etc.). In this context, this doctorate thesis discusses the importance of infrastructure for society with a special focus on energy, railway and motorway infrastructure. The central theme of sustainability, defined by the World Commission on Environment and Development (WCDE) as ''development that meets the needs of the present generation without compromising the ability of future generations to meet their needs’’, is also highlighted. Through their activities and relationships, organizations contribute positively or negatively to the goal of sustainable development. Sustainability becomes an integrated part of corporate culture. First research in this thesis describes how Artificial Intelligence techniques can play a supporting role for both maintenance operators in tunnel monitoring and those responsible for safety in operation. Relevant information can be extracted from large volumes of data from sensor equipment in an efficient, fast, dynamic and adaptive manner and made immediately usable by those operating machinery and services to support rapid decisions. Performing sensor-based analysis in motorway tunnels represents a major technological breakthrough that would simplify tunnel management activities and thus the detection of possible deterioration, while keeping risk within tolerance limits. The idea involves the creation of an algorithm for detecting faults, acquiring real-time data from tunnel subsystem sensors and using it to help identify the tunnel's state of service. Artificial intelligence models were trained over a sixmonth period with a granularity of one-hour time series measured on a road tunnel forming part of the Italian motorway systems. The verification was carried out with 3 reference to a series of failures recorded by the sensors. The second research argument is relates to the transfer capacities of high-voltage overhead lines (HVOHL), which are often limited by the critical temperature of the power line, which depends on the magnitude of the current transferred and the environmental conditions, i.e. ambient temperature, wind, etc. In order to use existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to critical power line temperatures, this work proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on a number of HV OHL located in different geographical locations in Italy. The objective is to estimate the temperature and ampacity of the OHL conductor, using a data-driven thermomechanical model with a bayesian probabilistic approach, in order to improve the confidence interval of the results. This work shows that it might be possible to estimate a spatio-temporal temperature distribution for each OHL and an increase in the threshold values of the effective current to optimize the OHL ampacity. The proposed model was validated using the Monte Carlo method. Finally, in this thesis is presented study on KPIs as indispensable allies of top management in the asset control phase. They are often overwhelmed by the availability of a huge amount of Key Performance Indicators (KPIs). Most managers struggle In understanding and identifying the few vital management metrics and instead collect and report a vast amount of everything that is easy to measure. As a result, they end up drowning in data, thirsty for information. This condition does not allow good systems management. The aim of this research is help the Asset Management System (AMS) of a railway infrastructure manager using business intelligence (BI) to equip itself with a KPI management system in line with the AM presented by the normative ISO 55000 - 55001 - 55002 and UIC (International Union of Railways) guideline, for the specific case of a railway infrastructure. This work starts from the study of these regulations, continues with the exploration, definition and use of KPIs. Subsequently KPIs of a generic infrastructure are identified and analyzed, 4 especially for the specific case of a railway infrastructure manager. These KPIs are fitted in the internal elements of the AM frameworks (ISO-UIC) for systematization. Moreover, an analysis of the KPIs now used in the company is made, compared with the KPIs that an infrastructure manager should have. Starting from here a gap analysis is done for the optimization of AMS

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems

Wood Properties and Processing

Wood-based materials are CO2-neutral, renewable, and considered to be environmentally friendly. The huge variety of wood species and wood-based composites allows a wide scope of creative and esthetic alternatives to materials with higher environmental impacts during production, use and disposal. Quality of wood is influenced by the genetic and environmental factors. One of the emerging uses of wood are building and construction applications. Modern building and construction practices would not be possible without use of wood or wood-based composites. The use of composites enables using wood of lower quality for the production of materials with engineered properties for specific target applications. Even more, the utilization of such reinforcing particles as carbon nanotubes and nanocellulose enables development of a new generation of composites with even better properties. The positive aspect of decomposability of waste wood can turn into the opposite when wood or wood-based materials are exposed to weathering, moisture oscillations, different discolorations, and degrading organisms. Protective measures are therefore unavoidable for many outdoor applications. Resistance of wood against different aging factors is always a combined effect of toxic or inhibiting ingredients on the one hand, and of structural, anatomical, or chemical ways of excluding moisture on the other

A computational framework for data-driven infrastructure engineering using advanced statistical learning, prediction, and curing

Over the past few decades, in most science and engineering fields, data-driven research has been becoming a promising next-generation research paradigm due to noticeable advances in computing power and accumulation of valuable databases. Despite this valuable accomplishment, the leveraging of these databases is still in its infancy. To address this issue, this dissertation investigates the following studies that use advanced statistical methods. The first study aims to develop a computational framework for collecting and transforming data obtained from heterogeneous databases in the Federal Aviation Administration and build a flexible predictive model using a generalized additive model (GAM) to predict runway incursions for 15 years in the top major US 36 airports. Results show that GAM is a powerful method for RI prediction with a high prediction accuracy. A direct search for finding the best predictor variables appears to be superior over the variable section approach based on a principal component analysis. The prediction power of GAM turns out to be comparable to that of an artificial neural network (ANN). The second study is to build an accurate predictive model based on earthquake engineering databases. As with the previous study, GAM is adopted as a predictive model. The result shows a promising predictive power of GAM with application to existing reinforced concrete shear wall databases. The primary objective of the third study is to suggest an efficient predictor variable selection method and provide relative importance among predictor variables using field survey pavement and simulated airport pavement data. Results show that the direct search method always finds the best predictor model, but the method takes a long time depending on the size of data and the variables\u27 dimensions. The results also depict that all variables are not necessary for the best prediction and identify the relative importance of variables selected for the GAM model. The fourth study deals with the impact of fractional hot-deck imputation (FHDI) on statistical and machine learning and prediction using practical engineering databases. Multiple response rates and internal parameters (i.e., category number and donor number) are investigated regarding the behavior and impacts of FHDI on prediction models. GAM, ANN, support vector machine, and extremely randomized trees are adopted as predictive models. Results show that the FHDI holds a positive impact on the prediction for engineering-based databases. The optimal internal parameters are also suggested to achieve a better prediction accuracy. The last study aims to offer a systematic computational framework including data collection, transformation, and squashing to develop a prediction model for the structural behavior of the target bridge. Missing values in the bridge data are cured by using the FHDI method to avoid an inaccurate data analysis due to biasness and sparseness of data. Results show that the application of FHDI improves prediction performances. This dissertation is expected to provide a notable computational framework for data processing, suggest a seamless data curing method, and offer an advanced statistical predictive model based on multiple projects. This novel research approach will help researchers to investigate their databases with a better understanding and build a statistical model with high accuracy according to their knowledge about the data

Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens