Parviz Soroushian - Tumblr

A licensed engineer in the state of Michigan, Dr. Parviz Soroushian has more than 35 years of teaching and research experience. He conducts research that contributes to advancements in the field of high-performance construction materials, polymer composites, nanotechnology, and infrastructure systems. Dr. Parviz Soroushian holds more than 20 patents in this area, including a patent for augmented cementitious binder composition and related methods. Dr. Soroushian has published more than 180 papers in a variety of scientific journals, in addition to presenting his research at professional conferences. A past chair of American Concrete Institute Committee 549, he holds fellow status with both the American Concrete Institute and the American Society of Civil Engineers. He also co-authored the book Structure-Property Relationships for Cement-Based Materials. In addition to serving as lead investigator on more than 100 research projects, he taught undergraduate and graduate-level courses in civil and environmental engineering for more than 35 years. His hobbies include reading historical books.

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Environmental Impacts of Artificial Intelligence (by Parviz Soroushian)

The rapid rise of artificial intelligence (AI) technologies marks a new phase of digital transformation that places a heavy burden on our environment. While digital applications can contribute to sustainability goals, accurately measuring their environmental impact remains a significant challenge. This brief review synthesizes key findings and methodologies for assessing the environmental impact of AI, extending beyond traditional focus areas to include a holistic life cycle assessment (LCA) approach.

There are growing environmental burden of digital technologies and AI:

Digital Transformation's Environmental Cost: The past few decades have been marked by the ever-increasing presence of digital technology. This growth, often called digital transformation, places a heavy burden on our environment.

AI as a New Phase of Impact: The emergence of AI is raising questions around sustainability due to the massive new use of these services and the IT infrastructures that support them, with their high demand for electricity and critical equipment.

Need for Comprehensive Assessment: The sustainability of computing technology cannot be addressed without a way to evaluate its environmental impact. This evaluation needs to consider multiple environmental impacts, not just carbon emissions.

Current environmental impact assessment methods for AI have some limitations:

Focus on Training Phase: Previous studies primarily focused on the electricity consumption of the machine learning (ML) training phase, as well as the associated greenhouse gas (GHG) emissions. This focus was partly because ML models were viewed as research projects, not mass-market products.

Inaccurate Electricity Consumption Estimates (TDP vs. PMs) - Thermal Design Power (TDP): This common method for estimating energy usage during training is limited, as it accounts only for GPUs and neglects other server components such as CPUs, memory buses, switches, and fans. It also overestimates GPU power consumption and is unreliable and depend[s] on workload and hardware.

Software-based Power Meters (Software PMs): While more precise than TDP, they are still incomplete and show a significant drawback: They require measurements during execution, making pre- or post-execution estimates impossible.

External Power Meters (PMs): These are the most accurate but require direct hardware access, which is often impractical.

Incomplete Life Cycle Perspective: Many studies often occurs solely through measuring electricity consumption. They frequently miss the full life-cycle impact of equipment, which is crucial due to the digital sector’s large embodied footprint. Studies that include the full life cycle are still rare and limited to the carbon cost of training and inference.

Beyond GHG Emissions: The digital sector's impact stretches beyond GHG emissions to, for example, the extraction of rare metals. GenAI also relies on and exerts pressure on the entire existing digital ecosystem, requiring terminals, such as smartphones and computers, for its users, as well as networks, to be accessible from its datacenters.

Life Cycle Assessment (LCA) can provide significant benefits towards assessment of the environmental impact of AI:

LCA as a Comprehensive Tool: LCA is a multi-criteria evaluation method based on the ISO 14040 and 14044 standards that aims to evaluate the potential environmental impacts of a product or activity, considering all of its life cycle phases: manufacturing, usage, and end of life.

Benefits of LCA for Digital Services: It enables a more comprehensive assessment by taking into account the complete life cycle and the different impact categories, thus avoiding focusing solely on the carbon emissions of the use phase.

It questions the sustainability of IT products and services, since it questions other sectors of activity using the same standard.

Application to Stable Diffusion: Recent research has applied improved LCA methodology to assess the environmental cost of Stable Diffusion, an open-source text-to-image generative deep-learning model, as an "end-to-end service."

Key Impact Categories Recommended: The methodology recommends measuring a minimum of three impact categories for AI: (1) Abiotic Depletion Potential (ADP): represents a decrease of minerals and metals resources. (2) Global Warming Potential (GWP): evaluates the contribution to climate change. (3) Primary Energy (PE): expresses the cumulative energy demand.

Service-Level Modeling: The proposed approach studies not only the impact of developing a model but also that of its deployment and use as a service, including user terminals, networks, web hosting, data processing, and model inference.

Preliminary findings of some life cycle analyses are:

Significant Environmental Impact: One year of Stable Diffusion as a service, with 75 million visits and 150 million pictures generated, resulted in significant environmental impact (463 tons of CO2 eq.).

Distribution of Impact - Terminals and Networks are Dominant: terminals and networks represent a significant share in the impact of a AI service: more than 85% of the ADP impact, more than 30% of the energy footprint, and 45% of the carbon footprint. This validates the need to include them in assessments, especially as their footprint grows with the number of users.

Datacenter Contributions: Datacenter-Inference and Web Hosting, along with Datacenter-Training and Data Storage, contribute the remaining percentages.

Operational vs. Embodied Footprint: The LCA differentiates between Operational (use-phase electricity) and Embodied (manufacturing and end-of-life of hardware) impacts.

Embodied Footprint is Substantial: While decarbonizing electricity sources reduces GWP, embodied carbon emissions and those produced on the user side remain significant.

Obsolescence and Hardware Life Extension: Extending the life of equipment is a direct lever for reducing both ADP and embodied emissions. GenAI, with its high electricity consumption... could indicate a reversal of tendencies regarding embodied carbon emissions, emphasizing the importance of extended hardware and software life cycles.

In conclusion, assessing the environmental impact of AI is a complex but crucial endeavor. By moving beyond limited, mono-category assessments and unreliable estimation methods, a holistic LCA approach reveals the substantial and diverse environmental costs of GenAI. This review highlights the significant impact of user terminals and networks, the importance of considering embodied emissions, and the need for accurate electricity consumption measurement. Addressing AI's sustainability challenges requires not only technological efficiency gains but also a focus on extending hardware life, optimizing existing resources, and a concerted effort towards greater transparency from the industry.

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AI-Powered Bridge Maintenance Management (by Parviz Soroushian)

Bridges are critical components of global infrastructure networks with substantial economic, social, and environmental implications. They are vulnerable to environmental factors and applied loads, leading to deterioration over time. Significant costs are associated with bridge maintenance, ranging from 0.4 to 2% of the initial construction cost annually. A substantial number of bridges globally are aging and structurally deficient, posing safety risks and requiring significant capital investment for repair or replacement. Bridge failures can be catastrophic, impacting lives.

Traditional maintenance strategies include reactive (which are expensive due to unplanned interventions) and scheduled (with limited impact on overall performance improvement).

AI offers a data-driven alternative to traditional, often subjective, maintenance approaches. AI models can harness data for actionable insights and future predictions, which are crucial for efficient asset management. A key advantage of AI models over physical models is that they do not require estimation or determination of inputs such as material properties, resulting in improved efficiency. AI models can efficiently process large volumes of data, minimizing computational challenges.

AI applications to bridge maintenance can be categorized into three main sub-domains:

Damage Identification (Defect Diagnosis): Focuses on locating and determining the extent of damage. This is the most researched area, with a significant emphasis on using AI algorithms for image classification, particularly for crack detection. AI techniques like Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Deep Learning models (e.g., YOLO, Faster R-CNN) are used to analyze bridge imagery for defects. Challenges include computational processing and data availability, as well as handling noise and environmental variations in images. Innovations include systems that not only detect but also measure cracks and generate textual descriptions of damage.

Condition Assessment: Aims to track changes in a bridge's condition to detect abnormal infrastructure states. Data-driven techniques define indices to estimate a bridge's reliability. AI models like Artificial Neural Networks (ANN) and regression models are used to predict condition ratings based on various input parameters such as age, traffic data, and past conditions.

Damage Prognosis (Deterioration Prediction): Focuses on predicting future damage trends and the remaining useful life of bridge components. Current methods involve analyzing historical data from Non-destructive Testing (NDT), physical testing, and visual inspections. AI-based approaches include Machine Learning (ML) and statistical methods for analyzing sensor data and predicting future defects, simulation and modeling (e.g., Finite Element Modeling), sensor networks for real-time monitoring, and computer vision analysis of time-series imagery.

Some challenges and suggested future directions in AI applications to bridge maintenance management include:

Data Availability and Quality: A significant challenge across all AI applications is the need for large, high-quality, and well-annotated datasets for training and validating models. Issues like data imbalance and missing data need to be addressed.

Computational Resources: Deep Learning (DL) methods, in particular, often require substantial computational resources for training and deployment.

Real-time Processing: Implementing AI models for real-time defect detection and condition monitoring presents computational challenges.

Integration with Existing Systems: Integrating AI-based tools with existing bridge management systems can be complex.

Interpretability and Transparency: Ensuring that AI model decisions are interpretable and transparent is crucial for gaining the trust of domain experts and for effective human-AI collaboration in decision-making.

Focus on Defect Prognosis: There is a need for more research on performance-based prognostic maintenance strategies to better predict the future condition of bridges and optimize long-term maintenance planning.

Multi-modal Data Fusion: Combining image data with other sensor data (e.g., from structural health monitoring systems) can enhance the accuracy and reliability of bridge damage detection systems.

Automation and Robotics: The integration of AI with robotics (e.g., UAVs for inspection) is paving the way for more automated and intelligent bridge inspection and monitoring.

In conclusion, there is a growing interest in transforming bridge maintenance management. While significant progress has been made in defect identification and condition assessment using AI, particularly through image processing, there is a crucial need for more research focused on performance-based prognosis and the development of interpretable AI models. Addressing the challenges related to data availability, computational resources, and integration will be essential for the widespread adoption of AI in ensuring the safety, longevity, and cost-effectiveness of bridge infrastructure. Future research should focus on bridging the identified gaps and further exploring the integration of AI with emerging technologies like robotics for a holistic and intelligent approach to bridge asset management.

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Artificial Intelligence in Transportation (by Parviz Soroushian)

AI is not a new concept, but its current surge in relevance is due to increased computing power, mass data storage, and algorithm innovation. AI can be defined as the simulation of human intelligence in machines programmed to think and learn like humans, encompassing abilities like visual perception, speech recognition, and decision-making. AI is valuable for its ability to process, categorize, and collate large volumes of data quickly, providing insights that would be impossible without technology.

Numerous areas have been identified where AI can augment or replace human actions in transportation. AI applications often involve identifying objects, recognizing speech, processing large datasets, learning from experience, and adapting to new environments. Examples include:

Accessible Transportation: AI-powered route planning, voice assistants for travelers with disabilities.

Advanced Driver Assistance and Automated Driving Systems: Adaptive cruise control, lane-keeping, and red light violation warnings.

Air Traffic Management: Predictive analytics, route optimization, automated decision support, and remote air traffic control towers.

Asset Management and Infrastructure Maintenance: Performance monitoring and prioritization of maintenance tasks.

Commercial Vehicle and Freight Operations: Route planning, fleet management, drayage optimization, and automation of highway inspections.

Cybersecurity: AI to enhance security in communications, navigation, and connected vehicles, but it also introduces new risks such as hacking.

Demand Forecasting and Scheduling: Using historical and real-time data to predict demand and adjust schedules accordingly.

Emergency Management and Incident Response: Supporting route optimization for responders and real-time incident reporting.

Predictive Maintenance: Predicting maintenance needs for vehicles and infrastructure.

Public Transportation and Airport Safety: Monitoring passenger behavior for suspicious activity.

Remote Sensing: Support for traffic monitoring, infrastructure inspections, and aerial mapping.

Traffic Safety: Real-time adjustments of traffic lights, predicting and preventing incidents.

Traveler Decision Support Tools: Providing information about route options, transit status, weather, and pricing.

Traveler Experience: Personalized information, deals, virtual assistants, and optimized seating.

Transit Operations and Management: Predictive maintenance, incident detection, and demand-responsive transit management.

Transportation Systems Management and Operations (TSMO): Optimizing network performance, managing work zones, and implementing congestion mitigation measures.

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Artificial Intelligence in Air Quality Control (by Parviz Soroushian)

Air pollution control is crucial for public health, environmental stability, and socio-economic well-being. There is a staggering toll of air pollution, with 4.2 million premature deaths annually attributed to conditions like heart disease, stroke, and lung cancer. There are also non-lethal health issues like asthma. Beyond human health, poor air quality damages ecosystems, causing harm to plant life, acidifying water, and reducing biodiversity. The formation of acid rain and the contribution of air pollution to climate change are important considerations.

While traditional measures like regulatory measures, monitoring stations, and engineering solutions are important, these methods have limitations including:

Limited Spatial Resolution: Monitoring stations offer a generalized view of air quality.

High Costs: Monitoring stations are expensive to install and maintain, limiting their coverage.

Reactive Approach: Traditional methods primarily respond to problems after they occur, rather than predicting or preventing them.

Inability to Adapt: Regulatory and engineering solutions struggle to adapt to fast-changing scenarios.

AI offers a promising alternative by processing vast amounts of data quickly, recognizing patterns, and predicting future air quality. AI is the capability of a machine to mimic human intelligence. It is an interdisciplinary field with roots in computer science, psychology, and philosophy. There are different types of AI, including:

Narrow AI (Weak AI): Designed for specific tasks, like voice recognition. This is the most common type of AI in use today.

General AI (Strong AI): Hypothetical AI that can learn and apply knowledge like humans do.

Machine learning (ML): The AI type that is particularly relevant to air pollution. Machine learning uses algorithms to learn from data and make predictions. ML algorithms learn from past computations and can produce reliable and repeatable results. ML can be further categorized into:

Supervised Learning: Trains on labeled datasets.

Unsupervised Learning: Learns from unlabeled datasets to identify patterns.

Reinforcement Learning: An agent learns by observing results of actions in an environment.

Deep Learning (DL): A subset of ML using neural networks with multiple layers for handling complex, high-dimensional data.

AI is increasingly used in environmental sciences for analyzing data, predicting trends, and guiding decision-making. A study using a Deep Learning model (LSTM) has shown high accuracy in predicting hourly PM2.5 concentrations, outperforming traditional methods. Machine Learning models, particularly Random Forest, have shown superior performance in estimating ground-level PM2.5 using satellite data, providing better spatial coverage. Effective for large datasets and complex patterns have been shown to have high accuracy in modeling air pollution.

There are limitations and challenges in application of AI models to air pollution control, including:

Data Dependency: AI models require high-quality and vast data for accuracy.

Computational Resources: AI models need considerable computational resources and technical expertise.

Method Selection: The choice of AI model depends on data characteristics and the prediction task.

Need for Technical Expertise: Requires knowledge of AI to build and utilize these models effectively.

Ethical and Privacy Concerns: Data collection and use must be handled ethically, and privacy must be maintained.

AI offers important advantages in air pollution control, including:

Superior Predictive Accuracy: AI algorithms, like Machine Learning and Deep Learning, have demonstrated greater accuracy than traditional statistical methods.

Large Data Processing: AI can handle vast and diverse datasets (e.g., meteorological, satellite, traffic, pollutant data) for a more comprehensive view of air quality.

Adaptability: AI algorithms learn from new data, allowing them to adapt to changing conditions.

Real-Time Monitoring: AI can process real-time data for instantaneous updates and forecasts.

Efficiency: AI can provide instant forecasts with minimal human intervention, once trained.

Scalability: AI can readily scale to handle larger datasets, unlike traditional methods.

The following measures can help with overcoming the challenges associated with the use of AI towards air pollution control.

Data Initiatives: Encourage data collection and sharing.

Model Interpretability: Develop more interpretable AI models.

Cloud Computing: Use cloud platforms for scalable solutions.

Training Programs: Educate people to implement AI solutions.

User-Friendly AI Tools: Develop tools to make AI accessible to non-experts.

In conclusion, AI is a transformative tool for air quality control, offering innovative and effective solutions. However, it needs further research and careful implementation, guided by principles of fairness and environmental justice, to achieve its full potential.

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Artificial Intelligence and Its Engineering Applications

This writing explores the impact of Artificial Intelligence (AI) on the engineering profession. It covers the evolution of AI, its applications in various engineering fields, the ethical and psychological considerations. The core argument is that AI is rapidly transforming engineering, presenting both opportunities and challenges that must be carefully addressed.

Intelligence may be defined as having six key traits: reasoning, understanding knowledge, the ability to plan, the ability to learn, the ability to communicate effectively, and integration of skills. AI seeks to replicate human cognitive abilities in machines. AI is categorized into three types based on the intelligence it exhibits:

Analytical AI: (Expert systems, supervised, unsupervised, and reinforcement learning) Primarily focused on cognitive intelligence, or pattern recognition and systematic thinking.

Human-Inspired AI: Includes both cognitive and emotional intelligence (adaptability, self-awareness, etc.).

Humanized AI: Incorporates cognitive, emotional, and social intelligence (empathy, teamwork, leadership).

Three evolutionary stages of AI may be identified:

Artificial Narrow Intelligence (ANI): Weak AI focused on specific tasks. Examples include facial recognition and route optimization.

Artificial General Intelligence (AGI): Strong AI capable of reasoning, planning, and problem-solving across a broad range of contexts. Experts predict this stage may be reached by 2040-2050.

Artificial Super Intelligence (ASI): High-level machine intelligence surpassing human cognitive abilities in almost all domains. Predictions suggest this could be attained around 2080.

There is a notable increase in AI related patents and publications related to Engineering. Additionally, revenue from AI-based software solutions is predicted to grow significantly, showcasing the rapid expansion of the field. VariousAI software platforms are now available that can assist Engineers. Examples include Microsoft Azure, TensorFlow, IBM Watson, and Infosys Nia.

Some major applications of AI in different Engineering disciplines are listed below.

Mechanical Engineering: AI is used in fracture mechanics (diagnostics, failure analysis), predictive maintenance of machinery, and designing smart machine tools. AI algorithms like k-NN, SVM, Deep Learning, Bayesian Networks, ANN, and CBR are utilized.

Civil Engineering: AI applications include structural engineering (modeling, design), construction management (monitoring), solid waste management (prediction, optimization), project management (risk assessment), and ocean engineering (analysis, simulation).

Electrical and Electronic Engineering: AI is applied in control engineering (plant-wide control), power systems (problem-solving), fault diagnosis and condition monitoring of electrical equipment, and image processing/computer vision.

Manufacturing: AI is being used to optimize the manufacturing process for Industry 4.0, make manufacturing more intelligent, and defend against cyber-attacks. AI is being integrated into Additive Manufacturing to optimize resource utilization, facilitate process control, and enhance data mining.

The ethical and psychological impacts of AI, briefly described below, should be treated carefully as AI applications in Engineering undergo significant growth.

AI Bias: AI algorithms are susceptible to biases present in their training datasets. This can lead to skewed or unfair decisions.

Military Applications: Ethical concerns arise from the use of AI in autonomous weapon systems, particularly around accountability for the actions of such systems.

Responsibility Gap: It is unclear who should be held responsible for decisions made by AI systems, such as a design flaw that causes a bridge to collapse.

Psychological Impacts: There is potential for technostress, burnout, and techno-addiction among engineers as a result of the rapid changes and demands created by AI. There is also potential for widening the economic gap, however that engineers are generally less likely to be affected due to the technical nature of their profession.

Need for Ethical Framework: There is a need for ethical frameworks for AI implementation, and educational efforts that integrate AI with ethical training to ensure proper use.

In conclusions, AI will significantly transform the engineering profession. AI is expected to increase productivity and shift engineers towards more knowledge-based tasks, which will likely require less time on manual labor. New disciplines and specializations in engineering are expected to emerge with advancements in AI. Predicting the future of AI and its precise impact is highly challenging, but the emergence of AGI and potentially ASI will fundamentally alter the role of an engineer.

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Artificial Intelligence in Structural Engineering (by Parviz Soroushian)

Artificial Intelligence is revolutionizing Structural Engineering practices by offering advanced mathematical frameworks and algorithms that enhance design, construction, and inspection processes. AI can automate design processes, explore design alternatives beyond human capabilities, and optimize design parameters.

There are challenges that need to be overcome towards broader applications of Artificial Intelligence in Structural Engineering. Understanding how AI models arrive at conclusions is crucial for safety and reliability. AI models require large datasets of high-quality data for accurate predictions. Overfitting during model training and selection needs to be mitigated, potentially through combining data-driven methods with domain knowledge.

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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.

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The Amazing Nacre (Mother of Pearl)

Nacre, also known as mother of pearl, is an organic–inorganic composite material produced by some molluscs as an inner shell layer. It comprises 95% calcium carbonate (aragonite) which, due to its layered microstructure, yields tensile strength, toughness and ductility levels as well as barrier and durability characteristics that far exceed those of monolithic aragonite or artificial inorganic materials such as concrete.

The superior engineering properties of nacre result from its controlled microstructure fored during a biomineralization process. This process involves controlled through-solution crystallization of amorphous materials (mostly calcium carbonate) to form intricate microstructures tailored towards serving structural, protective and other purposes.

Can we learn from biomineralization of nacre to produce improved material for construction of longer-lasting, safer and more sustainable infrastructure systems?

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Morphing Aircraft: The Need for Flexible Skin

The morphing aircraft attempts to break out of the confines of rigid structures with the intention of creating vehicles with multiple functional equilibria. The morphing aircraft is a multirole platform that changes its state substantially to adapt to changing operation environments, and is viewed as part of a revolutionary transformation from large, expensive piloted aircraft to smaller, autonomous types with combined roles and increased functionality.

Flexible skins constitute an enabling technology for morphing aircraft. The skin must be able to handle large in-plane deformations and out-of-plane aerodynamic loads while simultaneously carrying some shear loads. There are numerous design concepts that can be used for morphing aircraft, but most designs cannot effectively integrate skins. Elastomers constitute a promising group of materials for flexible skins; they are capable of elastically deforming by very large amounts without permanent changes in shape. The occasional cross-links within elastomers define an original shape which will be restored upon unloading.

Since elastomers are generally insulating, elastomeric skins can become charged in service; they could then behave as a capacitor and discharge in a single event causing considerable damage to the surrounding materials and the electronics on the vehicle. To mitigate electrostatic charge buildup, the surface resistivity of the skin should be lowered below a threshold level, which should be retained even under large elastomeric deformations. The conductivity of the elastomeric skin would also provide a level of protection against lightning strike.

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New Cement and Concrete Materials for a Sustainable Future

The global consumption of concrete, estimated at 30 billion tons per year, is second only to water. The desired balance of performance and cost offered by concrete has led to its prevalence as the most widely used material of construction. Concrete is the defining feature of our vast transportation, sewer, water, energy, defense, building and other infrastructure systems. While the structural performance and economics of concrete have led to its prevalent role in infrastructure systems for more than two centuries, the durability and sustainability of concrete are receiving increasing scrutiny. The rising cost of maintaining vast infrastructure systems in developed nations have led to growing demands for realizing improvements in service life and life-cycle economy by enhancing concrete durability. The hydraulic cement developed in this project offers twice the service life of today’s prevalent Portland cement at about half the production cost.

Our generation is also concerned with preserving the energy and natural resources as well as the environment for future generations. Production of cement and concrete account for about 5% of energy use, 10% of anthropogenic CO2 emissions and 30 billion tons/yr consumption of valuable natural resources worldwide. Improvements in the sustainability of cement and concrete production, while maximizing the use of market-limited industrial wastes, have emerged as major priorities within cement and concrete industries. Refined cement chemistries promise to have about 70% less energy content than today’s prevalent Portland cement, and a net negative carbon footprint. The tremendous volumes of cement consumed annually offer opportunities for value-added use of large CO2 volumes as a raw material in manufacturing of hydraulic cements with refined chemical composition. Tailoring of cement chemistry and its production condition can enable use of 1.5 billion tons/yr of carbon dioxide directly from combustion emissions or other abundant sources of CO2. Broad market transition of these technologies would reduce global energy use and anthropogenic CO2 emissions by about 3.5% and 10%, respectively.

The new classes of cement and concrete materials can replace close to 3.5 billion tons/yr of valuable natural resources used as raw materials in production of Portland cement with landfill-bound (or landfilled) raw materials such as market-limited grades of coal and biomass combustion ash, and metallurgical slags, in addition the CO2 content of combustion emissions. The emphasis on the use of landfill-bound and landfilled industrial wastes requires careful consideration of the safety attributes of the resulting concrete materials. The chemistry and structure of the hydration products of some of these new cements are particularly effective in immobilization of heavy metals and other hazardous wastes.

The market appeal of the next generation of sustainable hydraulic cements will be enhanced by their favorable economics, improved performance, and compatibility with common methods of concrete design, production and field construction. The compatibility of the new hydraulic cements with Portland cement will enable development of blended cements that would readily meet the specifications accepted by states and municipalities for seamless and expedient transition to construction markets.

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New Cement and Concrete Materials for a Sustainable Future

The global consumption of concrete, estimated at 30 billion tons per year, is second only to water. The desired balance of performance and cost offered by concrete has led to its prevalence as the most widely used material of construction. Concrete is the defining feature of our vast transportation, sewer, water, energy, defense, building and other infrastructure systems. While the structural performance and economics of concrete have led to its prevalent role in infrastructure systems for more than two centuries, the durability and sustainability of concrete are receiving increasing scrutiny. The rising cost of maintaining vast infrastructure systems in developed nations have led to growing demands for realizing improvements in service life and life-cycle economy by enhancing concrete durability. The hydraulic cement developed in this project offers twice the service life of today’s prevalent Portland cement at about half the production cost.

Our generation is also concerned with preserving the energy and natural resources as well as the environment for future generations. Production of cement and concrete account for about 5% of energy use, 10% of anthropogenic CO2 emissions and 30 billion tons/yr consumption of valuable natural resources worldwide. Improvements in the sustainability of cement and concrete production, while maximizing the use of market-limited industrial wastes, have emerged as major priorities within cement and concrete industries. Refined cement chemistries promise to have about 70% less energy content than today’s prevalent Portland cement, and a net negative carbon footprint. The tremendous volumes of cement consumed annually offer opportunities for value-added use of large CO2 volumes as a raw material in manufacturing of hydraulic cements with refined chemical composition. Tailoring of cement chemistry and its production condition can enable use of 1.5 billion tons/yr of carbon dioxide directly from combustion emissions or other abundant sources of CO2. Broad market transition of these technologies would reduce global energy use and anthropogenic CO2 emissions by about 3.5% and 10%, respectively.

The new classes of cement and concrete materials can replace close to 3.5 billion tons/yr of valuable natural resources used as raw materials in production of Portland cement with landfill-bound (or landfilled) raw materials such as market-limited grades of coal and biomass combustion ash, and metallurgical slags, in addition the CO2 content of combustion emissions. The emphasis on the use of landfill-bound and landfilled industrial wastes requires careful consideration of the safety attributes of the resulting concrete materials. The chemistry and structure of the hydration products of some of these new cements are particularly effective in immobilization of heavy metals and other hazardous wastes. The market appeal of the next generation of sustainable hydraulic cements will be enhanced by their favorable economics, improved performance, and compatibility with common methods of concrete design, production and field construction. The compatibility of the new hydraulic cements with Portland cement will enable development of blended cements that would readily meet the specifications accepted by states and municipalities for seamless and expedient transition to construction markets.

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Secret to Durability of Roman Concrete that Has Stood Test of Time for Over 2,000 Years Finally Discovered

The team mixed their own Roman concrete and it took only 2 weeks for a deliberately inflicted crack to close.

“When it comes to explaining Roman engineering, people looking to demonstrate their genius have a variety of use cases, but the secret to why their concrete has remained when even modern buildings crumble after just a few years of disuse has eluded scientists.

However at MIT, scientists have cracked their concrete code to show that Roman concrete was self-repairing—it could naturally close cracks in as little as 2 weeks…

Indeed, the Romans did things with concrete that modern societies haven’t—just look at the Pantheon in Rome which still has the world’s largest unreinforced concrete dome. Aqueducts built to water the city literally still do, 2,000 years and sometimes more after they were built.

It was once assumed that lime was added to concrete with water to create a paste in a process called slaking, but by using spectroscopic imaging, Masic and his colleagues at MIT, and elsewhere in Switzerland and Italy, have determined that the Romans heated the lime ash to high temperatures to create what is known today as “quicklime.”

“The benefits of hot mixing are twofold,” Masic says. “First, when the overall concrete is heated to high temperatures, it allows chemistries that are not possible if you only used slaked lime, producing high-temperature-associated compounds that would not otherwise form. Second, this increased temperature significantly reduces curing and setting times since all the reactions are accelerated, allowing for much faster construction.”

In such concrete as this, cracks that form allow the easy travel of the lime clasts into open space which, when coming in contact with water, seal and close the cracks. The team mixed their own Roman concrete and it took only 2 weeks for a deliberately inflicted crack to close.

By contrast a contemporary concrete block was made without quicklime and it never repaired itself.

As a result, the team are working to commercialize this as self-healing Roman concrete in order to reduce the 8% of global emissions attributed to the manufacture and laying of concrete in cities.” -via Good News Network, 1/9/23

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Sustainable Processing of High-Temperature Ceramics Using Microwave Energy

Processing of industrial ceramics for use in high-temperature service environments is currently accomplished by conventional heating that is energy-intensive and polluting. Microwave renders thermal and non-thermal effects that significantly benefit processing of ceramics. Microwave processing is adopted in industrial applications for realizing time, energy and cost savings, and for improving the end product quality and uniformity.

The heating mechanisms are different in conventional and microwave processing of materials. Conventional methods heat the surface and then rely on conduction, convection and radiation for transfer of heat into the material. Microwave, on the other hand, directly interacts with the material across its volume. Given the surface heat losses, heat transport under microwave radiation is from the core towards surface. The specific mechanisms and efficiency of microwave heating depend upon the material type. Microwave radiation also renders non-thermal effects that benefit diffusion, chemical reaction and densification phenomena.

Some advantages of microwave processing are: (i) reduced processing time, temperature and power consumption, and enhanced diffusion and reaction rates; (ii) finer, more uniform and nearly fault-free microstructures, yielding improved and more consistent physical and mechanical properties; (iii) reduced thermal stresses and heat-affected zones; and (iv) improved interfacial qualities resulting from selective hating of phases with higher microwave absorption. These advantages can reduce the energy demand and the corresponding polluting effects of processing ceramics by the currently prevalent conventional heating.

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More Durable Concrete: The Need & The Rationale

Concrete is the most widely used material of construction, with a consumption rate of 22 billion tons/yr. Production of concrete accounts for more than 4 billion tons/yr CO2 emissions and 20 billion GJ/yr energy use, most of which is associated with the manufacturing of cement. The concrete-based infrastructure supports diverse economic and social activities. The sustained growth in cement consumption over several decades reflects upon the buildup of an immense concrete-based infrastructure. Aging of this infrastructure has mounting economic and safety implications. 

While concrete occupies a significant volume of vast infrastructure systems, the expenditures on concrete materials constitute a relatively small fraction of the total infrastructure costs. Deterioration of concrete and corrosion of reinforcing steel under weathering, thermo-mechanical and chemical effects are key factors governing the maintenance and repair requirements and service life of the concrete-based infrastructure. More durable concrete materials would thus yield improportionally large life-cycle benefits. The improved service life of the concrete-based infrastructure would also translate into reduced CO2 emission and energy use, which would be magnified if Portland cement is replaced with a cementitious binder of reduced carbon footprint and energy content.

Deterioration of concrete and corrosion of the reinforcing steel are complex phenomena. Concrete is prone to microcracking at young age, primarily due to the internal and external restraint of shrinkage movements. The pore structure of concrete together with these microcracks facilitate transport of moisture, aggressive solutions and gases into concrete. These transport phenomena facilitate various mechanisms of concrete deterioration, including freeze-thaw damage, alkali-aggregate reaction, sulfate attack, carbonation, acid attack, and corrosion of the reinforcing steel. These deterioration mechanisms together with the growth of microcracks and cracks under mechanical (including fatigue) loading and restrained dimensional movements compromise the barrier qualities and accelerate the degradation of concrete. Abrasion, erosion and cavitation are also among the mechanisms of concrete degradation in such concrete-based infrastructure systems as pavements and hydraulic structures.

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Needs Related to Antimicrobial Surfaces

Surfaces in the building interiors can serve as reservoirs for pathogenic viruses and bacteria. As a result, infectious diseases are commonly transmitted through surfaces on which aerosols (ejected orally or nasally by already infected individuals) have settled. Antimicrobial surfaces can play key roles in preventing the spread of diseases via contaminated building surfaces.

Building occupants establish frequent contacts with such surfaces as (door, faucet, cabinet and toilet flush) handles, countertops, floorings, walls, and light switches. Technologies have been developed, but not broadly adopted in building applications, to make surfaces inherently antiviral and antibacterial. The cost burden of many such technologies have prohibited their broad building applications. There is a need for lower-cost methods of rendering building surfaces antimicrobial in both production and remedial settings.

Various test methods are available to assess the antimicrobial qualities of surfaces. These tests, however, cannot evaluate the long-term stability of antimicrobial surfaces in different service environments. Most interior building products remain in service for decades. There is a need for accelerated aging procedures for evaluating the long-term stability of antiviral and antibacterial surfaces. Such test methods can help with identifying economically viable means of providing interior building surfaces with sustained antimicrobial attributes. The resulting health benefits can then be weighed against the corresponding costs in an effort to persuade owners, occupants, builders and product manufacturers to employ antimicrobial surfaces. Development of building codes requiring antimicrobial surfaces would tie into these efforts towards high-impact implementation of the technology.

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Inorganic Polymers for Improving the Indoor Air Quality

Organic binders are used in building paneling, flooring and ceiling products. These binders could emit organic pollutants. The consistent drop in the air exchange rates of buildings has offset any benefits to the indoor air quality resulting from development of lower-emitting building materials. Enhancement of the indoor air quality is challenged by the diversity of organic pollutants and their emission from sources other than building products.

Recent developments in the field of inorganic polymers offer the potential to synthesize nearly zero-emission polymers for production of building products. Inorganic polymers span the range of binding and mechanical qualities offered by thermosets and thermoplastics. They are formed by alkali activation of aluminosilicate materials such as dehydrated clay, volcanic tuff and calcined fledspars which are prevalent in the earth crust, and various industrial by-products (e.g., metallurgical slags).  

Inorganic polymers offer advantages in terms of carbon footprint, energy content, cost, use of recycled and abundant raw materials, near-room-temperature processing, moisture and decay resistance, dimensional stability, resistance to mold growth, sound insulation, thermal stability, and fire resistance. The long-term durability of inorganic polymers enhances their life-cycle economic and sustainability advantages. In addition, inorganic polymers can be designed to provide qualities comparable to those of traditional ceramics (brick and tile) and also advanced ceramics at significantly reduced cost, energy content and carbon footprint.

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A Case for High-Performance Concrete

Various high-performance concrete materials with excellent strength, durability and barrier qualities are now available to us. Examples include high-strength concrete and ultra-high-performance concrete. Their initial costs tend to be higher than normal concrete. Concrete materials, however, account for a minor fraction of the total construction costs of most infrastructure systems. Adoption of high-performance concrete thus does not have a notable effect on the initial cost, but significantly benefits the service lives and reduces the maintenance costs of diverse infrastructure systems.