Near real-time damage diagnosis of building structures after extreme events (e.g., earthquakes) is of great importance in structural health monitoring. Unlike conventional methods that are usually time-consuming and require human expertise, pattern recognition algorithms have the potential to interpret sensor recordings as soon as this information is available. This paper proposes a robust framework to build a damage prediction model for building structures. Support vector machines are used to predict the existence as well as the probable location of the damage. The model is designed to consider probabilistic approaches in determining hazard intensity given the existing attenuation models in performance-based earthquake engineering. Performance of the model regarding accurate and safe predictions is enhanced using Bayesian optimization. The proposed framework is evaluated on a reinforced concrete moment frame. Targeting a selected large earthquake scenario, 6,240 nonlinear time history analyses are performed using OpenSees. Simulation results are engineered to extract low-dimensional intensity-based features that can be used as damage indicators. For the given case study, the proposed model achieves a promising accuracy of 83.1% to identify damage location, demonstrating the great potential of model capabilities.

Artificial intelligence (AI) isn't magic, it's just really complicated math, said Greg Mulholland, CEO and founder of Citrine Informatics (Redwood City, CA), at a press roundtable hosted by Lanxess (Cologne, Germany) at K 2019. But Mulholland's hosts seemed quite bedazzled by his AI-enabled platform, nonetheless. Lanxess is the first company to adopt Citrine's technology at scale, and Dr. Markus Eckert, Senior Vice President, Head of Business Unit Urethane Systems at Lanxess was eager to explain what it means for customers. Citrine is a Silicon Valley startup that couldn't be more niche: It has developed a platform that leverages data and AI specifically to accelerate the development of materials and chemicals. Citrine has been recognized for technology innovation by the World Economic Forum as a Tech Pioneer, and collaborates with world-class academic institutions such as Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.

Digitalization is occurring across all manufacturing industries, and the coatings sector is no exception. The quantity of data that can be leveraged to improve all business activities--from new product development to production to customer service--is increasing dramatically. The challenge is to determine where and how to apply technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) and how to make the data on hand relevant to the problem or question of interest. These questions and others were considered by members of the coatings value chain and their insights are presented below. What types of Big Data can be leveraged by the coatings industry to facilitate research, development, and innovation in general? Sapper, Cal Poly: We need to be asking three questions when it comes to data needs in our industry. What data do we have? What data do we need? And what questions are we trying to answer? A lot of valuable data already exists, but it is tied up in reports, published literature, or subject matter expertise. The data is there, but not collected in a way that allows helpful artificial intelligence and machine learning projects to be performed. Understanding what type of data is needed for a particular project is the first step in identifying where that data might already exist.

Our Random Forest model predicts a 73% probability of the Oakland Raiders keeping it within the 4.5 points being offered at Westgate.

People living in neighborhoods with poor building standards are more likely to be killed by a disaster. Their homes, too often built in a cheap or makeshift manner, are susceptible to dangerous events like earthquakes, hurricanes, and landslides. These (typically poor) inhabitants make up a disproportionate number of the 1,300,000 lives taken by disasters in the last 25 years. Families move into poor urban communities seeking better jobs and opportunities but don't possess the money or technical knowledge to access safe, resilient housing. Governments and communities strive to retrofit these structures for safety -- usually a simple, cheap, and effective process -- but dangerous housing remains a dilemma.

A birds-eye view of three of SpaceX's glimmering Starship spacecraft shows that the vessels are slowly but surely coming together. In aerial footage taken by videographer John Winkopp, the Starship craft's shimmering stainless steel body can be seen taking ship at the company's facility in Cocoa, Florida. As reported by CNBC, the video also shows the first stainless steel bands of another Starship prototype, the Mark 4, being assembled. The progress gives credence to a claim from SpaceX CEO and Tesla founder, Elon Musk, who claimed that the next-generation craft will be ready for test flights between October and November. An FCC filing surfaced in September that revealed SpaceX requested permission to fly Starship more than 12 miles into orbit and then land the craft back down in the same spot.

In areas like the Caribbean that face considerable risk from natural hazards like earthquakes, hurricanes, and floods, these forces of nature can have a devastating effect. This is especially true where houses and buildings are not up to modern construction standards, often in poor and informal settlements. While buildings can be retrofit to better prepare them for disaster, the traditional method for identifying high-risk buildings involves going door to door by foot, taking weeks if not months and costing millions of dollars. This is where AI can help. WeRobotics and the World Bank Global Program for Resilient Housing have teamed up to prepare aerial drone imagery of buildings across the Caribbean annotated with characteristics that matter to building inspectors.

The "Leveraging Artificial Intelligence for Materials Design and Production" report has been added to ResearchAndMarkets.com's offering. Artificial intelligence (AI)- and machine learning (ML)-based technologies are being leveraged for materials research and are replacing experimental and simulation-based research approaches. The need to accelerate materials discovery and the desired accuracy in the properties of materials is driving researchers to seek more granular insights from their experimentations. The development of new materials is a growing field and challenges such as database availability and practical viability of theoretically designed materials are still to be addressed. Multiple research studies from research institutes and companies have developed techniques to use AI-based techniques for the discovery of new molecules that can address existing challenges in the development of new materials and for aiding their mass production.

I listened carefully to Yann Fleureau's speech during the DATADRIVENPARIS event about his 4 past years as a Co-Founder and CEO of CardioLogs and his journey towards building and selling an AI-based Clinical Decision Support System (CDSS) for Clinicians in the Cardiology space. CardioLogs is a Paris-based Startup building Deep-Learning Algorithms for ECG (EKG) analysis. They have raised approximately 10M$ to date and have won approval for commercialization in Europe of the first medical grade deep-learning technology in 2016 and the second in the US in 2017. Yann is a graduate from the prestigious Polytechnic School of Paris (X) and passionate about New Technology & Medicine (https://cardiologs.com/). The last 4 years of CardioLogs illustrate well the challenges of implementing an AI-based solution in clinical practice.

Enterprise Artificial Intelligence provider Noodle.ai and SMS group, an expert in digitalization for steel and nonferrous-metals production, are joining forces to make production at Big River Steel more resource-efficient and energy-saving in the future . To this end, the two partners plan to integrate Noodle.ai's Using sensor data, the signal series and historical data of some 50,000 different systems will be analyzed on-site. Further external data sources will provide the AI with additional values relating to production processes and forecasts and possible corrective measures. It is hoped that these insights will, for instance, help minimize transition losses in terms of steel grade or product thickness or width and predict the energy requirements of production to the nearest hour.