Engineering Skills and Artificial Intelligence (by Parviz Soroushian)

 

This writing highlights a critical skills gap regarding the development and deployment of AI systems. AI is not solely about machine learning algorithms, but a complex interplay of software, sophisticated electronics, connectivity, machines, and infrastructure. AI can realize its full potential in Engineering applications if this broader AI systems engineering skill shortage is addressed. There is an urgent need for collaborative efforts between industry, government, and academia to create relevant educational and training programs, and for upskilling the current workforce to meet the demands of the AI era.

AI is a revolutionary technology with the potential for profound impact on the economy, society, and environment. One should make a critical distinction between focusing solely on machine learning algorithms and the broader AI ecosystem. AI systems combine algorithms with sophisticated electronics, pervasive connectivity, electro-mechanical modules and machines, and physical infrastructure. A complete view of AI should include an understanding of the end-to-end systems that integrate AI. There is a serious gap between the skills needed for the future and those currently available. While there is a focus on training and development within the broader AI skill sets, a gap has been noted in specifically training for AI systems.

The integration of AI requires a systems engineering mindset, with engineers prepared to work across different compute engines, understand algorithms, and have skills in hardware, software, and compilers. Engineers should also develop skills in digital signal processing (DSP), and other aspects of electronics. This can be accomplished through collaborative efforts between industry and academia. A closer relationship between industry and higher education can help with keeping the course contents current, and providing students with relevant experience. There are significant benefits to be realized from development of apprenticeships, especially at the degree level, as a way of bringing new talent into the AI field. Most companies are planning to up-skill and re-skill their current workforce to deal with this technological shift. Companies in the technology sector (semiconductors, electronics, sensors) are driving the evolution of AI systems. The future of AI will depend on these sectors, so focus should be placed on them.

In conclusion, the focus at the government level needs to be on AI systems and engineering skill sets, not just AI algorithms. It is crucial to adapt educational and training programs to meet the diverse skills required for AI systems, creating graduates that are ready for the industry. Greater collaboration between industry and academia is necessary to ensure training and education is relevant to current and future needs, for both those entering the workforce and those needing to up-skill.

Artificial Intelligence and Bridge Infrastructure Management (by Parviz Soroushian)

 

Bridges are essential components of civil infrastructure, requiring robust asset management to ensure safety and longevity. Traditional methods for monitoring bridge health are time-consuming, expensive, and prone to human error due to the large volume of data involved.

Artificial Intelligence (AI) offers efficient and accurate solutions for processing data from bridge monitoring systems, identifying patterns, and providing insights into structural health. Despite the potential of AI, there are still open research challenges that need to be addressed to fully utilize AI in bridge asset management.

Bridges are subject to damage from various factors including varying temperatures, road salt, heavy traffic, and abrasive forces. Structural health monitoring (SHM) systems are crucial for measuring bridge data, processing it, and assessing the health of the structure. AI algorithms can efficiently process large datasets collected by SHM systems, overcoming the limitations of manual processing. AI-powered systems can help identify patterns in data, predict potential problems, and optimize maintenance schedules, ultimately enhancing the lifecycle of bridges.

There are needs for further research in the field of AI-powered bridge asset management. This research could focus on areas such as:

·        Developing more robust and accurate AI algorithms for specific types of bridge damage.

·        Integrating AI systems with existing SHM infrastructure.

·        Addressing ethical and safety concerns related to AI-driven decision-making in bridge maintenance.

In conclusion, use of AI in bridge infrastructure management presents a promising solution to the challenges of traditional monitoring methods. By leveraging the power of AI, it is possible to create more efficient, accurate, and cost-effective systems for ensuring the safety and longevity of bridges. Continued research and development in this field will be crucial for unlocking the full potential of AI in maintaining critical infrastructure.

A Robust Process for Effective Carbon Capture (by Parviz Soroushian)

A complete replacement of fossil fuels by renewable sources of energy is not feasible in the short term. Therefore, there is a need to equip fossil fuel power plants with CO₂ capture and sequestration (CCS) capabilities in order to prevent the projected 2°C global warming by 2100. Carbon capture and sequestration would allow for continued use of fossil fuel until a deeper penetration of renewable energy sources into the grid is realized in an orderly fashion. Widespread adoption of CCS technologies would benefit from the availability of processes that lower the cost. Developments in advanced CCS materials and processes are needed in order to achieve this goal. One option is to develop a robust, economical and efficient CCS process based upon the principles of mechanochemistry.

The mechanochemical CCS process will take place in ambient condition within an energy-efficient mill incorporating a nearly dry inorganic sorbent. In the CO₂ sorption step (A), the input of mechanical energy will disturb the structure of the solid sorbent, and will drive CO₂ dissolution and deep diffusion of carbonate anions. This process offers favorable kinetics comparable to the dissolution of carbon dioxide in melts at magmatic temperatures and pressures. The disturbed structure of the sorbent and the dissolved nature of the captured CO₂ enable desorption within the same (mill) chamber at a moderately elevated temperature produced efficiently via microwave irradiation (B); rotation of the mill at a low speed benefits the uniformity of microwave exposure. The mild desorption conditions benefit the stability of the sorbent under repeated sorption-desorption cycles. The mechanochemical CCS process is robust, and can be tailored to accommodate different solid sorbents and the combustion emissions of different fossil fuels. Scale-up of the process benefits the mechanochemical effects by raising the intensity of energy input. 

Building and Furnishing Materials for Improved Indoor Air Quality

 

People spend approximately 90% of their time indoors at homes, public buildings and offices where concentrations of many pollutants, including volatile organic compounds (VOCs), are frequently higher than the outdoor urban air. Adverse health effects can result from the buildup of several VOCs in the indoor air, including formaldehyde, benzene, toluene and xylene. Building materials and furnishings are sine sources of VOC emissions. Development of commercially viable building and furnishing materials with reduced emissions would allow for implementing more stringent codes that enable improvement of the indoor air quality.

Reconstituted (engineered) wood products have emerged in recent decades as popular building and furnishing materials. They are composed of wooden elements of various size and shape, bonded by a synthetic resin. Examples of reconstituted wood products are particleboard, medium density fiberboard (MDF) and hardwood plywood (made commonly with urea-formaldehyde resins), and oriented strandboard and softwood plywood (made with phenol-formaldehyde resins). Reconstituted wood products constitute the majority of indoor surfaces (building products, cabinets and furniture). They can emit a variety of VOCs into the indoor air environment; examples include formaldehyde, acetone, hexanal, propanol, butanone, benzene and benzadehyde. Synthetic resins are the primary sources of any formaldehyde emission from reconstituted wood products.

Some adverse effects of reconstituted wood products on the indoor air quality have created a need for development of low-emission reconstituted wood products, and for development of building codes that encourage their broad adoption. This need can be addressed in a fundamental way through development of lower-emission binders that meet relevant performance, cost and sustainability requirements. Development of refined inorganic polymer binders and compatible processing techniques could be a viable approach to addressing the need for reconstituted wood products that are friendlier to the indoor air quality.