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Digital transformation of bridges inspection, monitoring and maintenance processes
Published in Hiroshi Yokota, Dan M. Frangopol, Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, 2021
T.N. Bittencourt, M.M. Futai, A.P. da Conceição Neto, D.M. Ribeiro
Under the current level of social conscience and knowledge about infrastructure, maintenance demands Lacer´s statement sounds very naive for our present society. The challenges to make our infrastructure more reliable and resilient are considerable, but they are fascinating. In this context, the introduction of new technologies and digital transformation possibilities will make the whole process more accessible and comprehensive. Digital Transformation can reduce maintenance costs (avoiding unnecessary maintenance events), improve system availability, while reducing operational losses. The use of Big Data Analytics techniques, incorporating Artificial Intelligence and Machine Learning, are innovative solutions that can be introduced. The adoption of Digital Twins, that incorporate all these tools, can lead to a reduction in the total cost, allowing predictive and proactive maintenance. The future is there to be created.
Renewable digital transformation and cybersecurity management
Published in Henry K. H. Wang, Renewable Energy Management in Emerging Economies, 2020
One of the most serious organisation hurdles is the organisational resistance to changes. Management should recognise that the majority of staff will normally have big concerns about changes and uncertainties which could challenge their roles or reduce their job security. Digital transformation, by its very nature, could change a lot of job roles and could also reduce some job securities. The resistance to changes could manifest itself in a myriad of ways in companies. These could include digital projects taking a long time to develop and implement. They could also be delayed by inadequate expertise and resource availabilities. There could also be different technical, commercial and legal review requirements in companies which could slow down the digital transformation.
Why a Digital Transformation?
Published in Yves Caseau, The Lean Approach to Digital Transformation, 2022
The first is the ability of digital and exponential technologies to go even further in automating factory workstations. At first glance, this is a development in perfect continuity with what has been happening for many decades, namely the automation of production. If we take a closer look, which we will do in Chapter 5, the capabilities of AI and ML, such as image recognition, allow us to go much further and automate, or assist in the optimization of, much more complex tasks than what we knew how to do ten years ago. Digital transformation allows us to control manufacturing capacities in a reactive way and to adapt production capacity to customer demand.19
Challenges in the application of digital transformation to inspection and maintenance of bridges
Published in Structure and Infrastructure Engineering, 2022
Marcos Massao Futai, Túlio N. Bittencourt, Hermes Carvalho, Duperron M. Ribeiro
Digital transformation, associated with innovative computational techniques, is rapidly paving the way in this direction, providing the following interesting features: (a) Collection, organization and analysis of existing information (design, tests, inspections. monitoring and other information available); (b) Traditional and non-invasive techniques for inspection with the use of new technologies; (c) More affordable SHM (Structural Health Monitoring) systems and the Internet of Things (IoT) to generate new monitoring and analysis possibilities; (d) Implementation and application of automatic methodologies based on performance indicators and metrics for detailed classification and diagnosis of the bridge integrity, including the bridge deck, piers, abutments, foundations and bearings (Ghosn et al., 2016); (e) Development of Digital Twins for bridges and the use of Extended Reality (Zhu, Liu, & Xu, 2019); (f) Integration of the collected information. Digital Transformation can reduce maintenance costs (avoiding unnecessary maintenance events), improve system availability while reducing operational losses. The use of Big Data Analytics techniques, incorporating Artificial Intelligence and Machine Learning, are innovative solutions that can be introduced. The adoption of Digital Twins, which incorporate all these tools, can lead to a reduction in the total cost, allowing predictive and proactive maintenance.
Industrial ontologies for interoperability in agile and resilient manufacturing
Published in International Journal of Production Research, 2022
Farhad Ameri, Dusan Sormaz, Foivos Psarommatis, Dimitris Kiritsis
Manufacturing industry is undergoing profound changes fuelled by the emergence of disruptive digital technologies such as cloud computing, artificial intelligence and machine learning (AI/ML), Industrial Internet of Things (IIoT), augmented and virtual reality (AR/VR), collaborative robotics, and blockchain (Rosin et al. 2020). The digital transformation is reshaping the way products are designed, manufactured, distributed, and maintained, potentially resulting in more agile, resilient, and sustainable manufacturing systems and supply chains (Mittal et al. 2019). One major consequence of digitisation in the industry is exponential growth in volume, variety, and availability of data at an unprecedented level (Xu, Xu, and Li 2018). The ability to efficiently and effectively collect, process, analyse, communicate, and store data is key to the successful implementation of smart manufacturing systems in Industry 4.0 era. However, by increase in the size and complexity of manufacturing systems, and the diversity and heterogeneity of participating stakeholders and their underlying software tools and legacy systems, knowledge and data sources become more fragmented, siloed, and disintegrated. Lack of semantic coherence and integrity in data hampers effective collaboration, communication, and decision making across different phases of product life cycle. The challenge is to bring about digital continuity and interoperability across various software agents and computational tools that are designed for different tasks and use different abstractions, viewpoints, and semantics for interpretation of data.
Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings
Published in Journal of Decision Systems, 2021
Eman Saeed Alamoudi, Norah Saleh Alghamdi
Digital transformation (e.g. online shopping and social media platforms) integrates digital technology into businesses work in order to solve problems related to old operating methods, attract new opportunities and deliver valuable services to customers (Verhoef et al., 2021). Furthermore, incorporating big data analysis with business processes will lead to more accurate results-producing through decision support systems (DSS; Osuszek et al., 2016). However, in the case of online review, the vast number of reviews has made it difficult for interested parties to read all the reviews and determine the opinions and the quality of products. Subsequently, the artificial intelligence (AI) branches such as sentiment analysis is simply an automated way for the classification of emotions in the review text. It is a method of extracting information from the text. It seeks to turn the large and unstructured dataset into observable indices of sentiment (e.g. positive, negative, or neutral). The extracted information can be summarised opinions presented in the form of numbers or graphs, which makes producing the required information by interested persons, such as managers or customers, easy and quick. This underlines the inspiration behind sentiment analysis and generates greater interest in this field of research.