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Civil Engineering

Faster Planning for Complex Problems with Machine Learning

Researchers developed a machine-learning-guided technique to solve complex, long-horizon planning problems more efficiently than some traditional approaches, while arriving at an optimal solution that better meets a user’s goals.

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The faster planning system developed by MIT researchers uses machine learning to reduce solve time by up to 50 percent and produce a solution that better meets a user’s objective, such as on-time train departures. The new method could also be applied to other complex logistical problems like scheduling hospital staff or assigning airline crews.

Engineers often break down these kinds of problems into a sequence of overlapping subproblems that can each be solved in a feasible amount of time. However, the overlaps cause many decisions to be needlessly recomputed, making it take much longer to reach an optimal solution.

The researchers’ new approach learns which parts of each subproblem should remain unchanged and freeze those variables to avoid redundant computations. A traditional algorithmic solver then tackles the remaining variables.

“This is a very complex combinatorial scheduling problem,” says Cathy Wu, a member of the Laboratory for Information and Decision Systems at MIT. “Our approach can be applied without modification to all these different variants.”

The researchers’ technique, which they call learning-guided rolling horizon optimization (L-RHO), teaches a machine-learning model to predict which operations should be recomputed when the planning horizon rolls forward.

To test their approach, the researchers compared L-RHO to several base algorithmic solvers and specialized solvers. It outperformed them all, reducing solve time by 54 percent and improving solution quality by up to 21 percent.

Their method continued to outperform all baselines even when tested on more complex variants of the problem, such as factory machines breaking down or extra train congestion.

“Our approach can be applied without modification to all these different variants,” says Wu. “It even outperformed additional baselines we created to challenge our solver.”

L-RHO can also adapt if the objectives change, automatically generating a new algorithm to solve the problem – all it needs is a new training dataset.

In the future, the researchers want to better understand the logic behind their model’s decision to freeze some variables, but not others. They also want to integrate their approach into other types of complex optimization problems like inventory management or vehicle routing.

This work was supported by the National Science Foundation and MIT’s Research Support Committee, among others.

Civil Engineering

The Sinking Cities of America: A Study Reveals Widespread Land Movement Across 28 Major U.S. Metropolises

A new study of the 28 most populous U.S. cities finds that all are sinking to one degree or another. The cities include not just those on the coasts, where relative sea level is a concern, but many in the interior. Furthermore, using newly granular data, the study finds that some cities are sinking at different rates in different spots, or sinking in some places and rising in others, potentially introducing stresses that could affect buildings and other infrastructure.

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The study, published in Nature Cities, reveals that all 28 most populous U.S. cities are experiencing some degree of land movement, with the majority sinking at varying rates due to a combination of factors including groundwater extraction, climate change, and human activities such as construction and urbanization.

Lead author Leonard Ohenhen, a postdoctoral researcher at Columbia Climate School’s Lamont-Doherty Earth Observatory, notes that as cities continue to grow, subsidence can become more pronounced, producing stresses on infrastructure that may exceed safety limits. “We will see more cities expand into subsiding regions,” he says.

The study uses satellite data to map land movements in the 28 cities, including Houston, which is experiencing some of the most rapid sinking, with over 40% of its area subsiding more than 5 millimeters per year. Other Texas cities, Fort Worth and Dallas, are also among the fastest-sinking, while areas around New York’s LaGuardia Airport and parts of Las Vegas, Washington, D.C., and San Francisco are experiencing localized fast-sinking zones.

Researchers found that groundwater removal for human use was responsible for 80% of overall sinkage, with compaction below ground level causing subsidence at the surface. Climate-induced droughts in some areas will likely worsen subsidence in the future, says Ohenhen.

The study also reveals that natural forces are at work in some areas, such as the weight of ancient ice sheets that once covered much of interior North America. Even today, some cities like New York, Indianapolis, Nashville, Philadelphia, Denver, Chicago, and Portland are still subsiding due to these bulges, with rates ranging from 1 to 3 millimeters per year.

The researchers emphasize that continued population growth and water usage will likely exacerbate subsidence in the future. They recommend that cities focus on solutions such as land raising, enhanced drainage systems, and green infrastructure to mitigate flooding, and retrofitting existing structures to address tilting hazards.

Ohenhen concludes, “We have to move to solutions.” The study was coauthored by researchers from various institutions and provides a valuable resource for policymakers and urban planners to address the challenges posed by subsidence in major American cities.

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Artificial Intelligence

The Hidden Barrier to Advanced Robotic Touch

Researchers argue that the problem that has been lurking in the margins of many papers about touch sensors lies in the robotic skin itself.

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The development of advanced robotic touch has been hindered by a seemingly innocuous yet critical issue – the insulating layer in robotic skin. Researchers at Northwestern University and Tel Aviv University have successfully overcome this barrier, paving the way for low-cost solutions that enable robots to mimic human touch.

In their study, the researchers observed that inexpensive silicon rubber composites used to make robotic skin host an insulating layer on both top and bottom surfaces. This prevents direct electrical contact between the sensing polymer and the monitoring surface electrodes, making accurate and repeatable measurements impossible. By eliminating this error, cheap robotic skins can now allow robots to sense an object’s curves and edges, essential for proper grasping.

The research team, consisting of electrical engineers and polymer materials scientists, shed light on this problem in a paper published in Advanced Electronic Materials. The study highlights the importance of validating electrical contacts, which might unknowingly obscure device performance.

“A lot of scientists misunderstand their sensor response because they lump together the behavior of the contacts with the behavior of the sensor material, resulting in inconsistent data,” said Matthew Grayson, professor of electrical and computer engineering at Northwestern’s McCormick School of Engineering. “Our work identifies the exact problem, quantifies its extent both microscopically and electrically, and gives a clear step-by-step trouble-shooting manual to fix the problem.”

The researchers detected that adding electrically conducting fillers like carbon nanotubes to rubber composites creates an ideal candidate for touch sensors. However, this material needs electrical signals, which are blocked by the insulating layer. By sanding down the ultrathin insulation layer, the team achieved a stronger electrical contact and calibrated the thickness of the insulating layer.

The collaboration between Northwestern University and Tel Aviv University is essential in addressing the “contact preparation” challenge. The researchers relied on each other’s expertise to prepare materials and study their properties, leading to consistent results across various variables.

As awareness spreads among researchers about the issue of reproducibility in touch sensing literature, new publications can be more rigorously relied upon to advance the field with new capabilities. The research was supported by various organizations, including the U.S. National Science Foundation, Northwestern University, and Tel Aviv University through the Center for Nanoscience & Nanotechnology.

The breakthrough has significant implications for robotics development, enabling robots to sense and interact with their environment more effectively. By overcoming this critical barrier, researchers have opened up new possibilities for advanced robotic touch, paving the way for future innovations in robotics and beyond.

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Aerospace

“Challenging the Classics: Researchers Reveal New Insights into Material Deformation under Stress”

Scientists have expanded on a longstanding model governing the mechanics behind slip banding, a process that produces strain marks in metals under compression, gaining a new understanding of the behavior of advanced materials critical to energy systems, space exploration and nuclear applications.

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Researchers at the University of California, Irvine (UCI) have made a groundbreaking discovery in the field of materials science. By expanding on a classic model developed over 70 years ago, scientists in UCI’s Samueli School of Engineering have gained new insights into the behavior of advanced materials critical to energy systems, space exploration, and nuclear applications.

The traditional Frank-Read theory attributed slip band formation to continuous dislocation multiplication at active sources. However, the UC Irvine team found that extended slip bands emerge from source deactivation followed by the dynamic activation of new dislocation sources. This process was observed at the atomic scale through mechanical compression on micropillars made of a chromium-cobalt-nickel alloy.

Using advanced microscopy techniques and large-scale atomistic modeling, researchers were able to visualize the confined slip band as a thin glide zone with minimal defects and the extended slip band with a high density of planar defects. This understanding has shed new light on collective dislocation motion and microscopic deformation instability in advanced structural materials.

Deformation banding, where strain concentrates in local zones, is a common phenomenon in various substances and systems, including crystalline solids, metals, granular media, and even geologic faults under compressive stress. The discovery of extended slip bands challenges the classic model developed by physicists Charles Frank and Thornton Read in the 1950s.

“This foundational knowledge will accelerate the discovery of materials with tailored and predictable mechanical properties to meet the rising demand for advanced materials resilient to extreme environments across energy and aerospace sectors,” said corresponding author Penghui Cao, UC Irvine associate professor of mechanical and aerospace engineering.

The research was funded by the U.S. Department of Energy, UC Irvine, and the National Science Foundation (through the UC Irvine Center for Complex and Active Materials). The project involved graduate students, research specialists, and other professors from UCI’s Department of Mechanical and Aerospace Engineering and Department of Materials Science and Engineering.

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