On September 11, 2023, Storm Daniel unleashed unprecedented rainfall over the Wadi Derna watershed, triggering one of the most devastating floods in modern history, striking Derna, a coastal city in Libya. This study reconstructs the disaster using an integrated modeling approach that combines satellite imagery, hydrologic, hydraulic, and geotechnical simulations, machine learning, eyewitness accounts, and digital elevation data to assess the impact of cascading dam failures. Our findings reveal that the region’s dams, even if structurally sound, would have provided minimal protection against the extreme runoff. However, their failure unleashed a destructive surge wave, amplifying the disaster’s magnitude and devastation. Here, we show that the collapse of aging flood control infrastructures, compounded by inadequate risk assessment and emergency preparedness, dramatically escalated the disaster’s impact. Our findings underscore the urgent need for systematic dam safety evaluations, enhanced flood forecasting, and adaptive risk management strategies that address climate extremes and infrastructure vulnerabilities.

Expansive Yazoo clay soil is susceptible to volumetric deformation and is dominant in central Mississippi and other neighboring southern states of the United States. Recurring shrink-swell behavior causes a significant problem to infrastructures in the area. Although Yazoo clay causes a significant problem in the deep southern states, limited study has been conducted on the behavior of Yazoo clay, especially in the presence of rainfall. The objective of this current study is to investigate the coupled effect of changes in void ratio due to wet-dry cycles and rainfall on the stability of highway slopes made of Yazoo clay. The finite element method in Plaxis 2D by Bentley System (https://www.plaxis.com/) has been utilized to investigate the coupled effect of changes in mechanical properties and rainfall using flow-deformation and stability analysis. Reconstituted expansive clay soil samples were used for the laboratory experiment. The reconstituted Yazoo clay samples were subjected to 3, 5, and 7 wetting and drying cycles in an enclosed chamber for a 24-h period. The axial deformation of the samples and the change in void ratios at each number of the cycle was closely monitored. The strength change at each wet and dry cycle was also investigated and used for slope stability analysis in the presence of rainfall. The test results indicate that the void ratio increases with the increasing number of wet-dry cycles. A continuous increment in void ratios from 0.99 in an undisturbed state with no wet-dry cycle to 1.49 at the 7th wet-dry cycle, indicating a 48.9% increase, as the wetting and drying cycle increases was recorded; in turn, decreasing the cohesion of the soil by 77%. The factor of safety considering the effect of two total rainfall periods of Rv = 126.2 mm (2 h) and Rv = 271.7 mm (3 days) reduced from 1.7 to 1.2 and 1.68 to 1.02, considering the effect of the 7th wet-dry cycle at the topsoil. The changes in the void ratio due to the wetting and drying cycle of Yazoo clay soil reduces the shear strength to a fully softened condition, increasing the possibility of slope failure. This condition further worsens in the presence of a perched water condition due to the infiltration of rain water.

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to solving hard engineering problems, including that of structural health monitoring (SHM). SHM consists of automating the condition assessment task of civil, health, mechanical, and aerospace systems using measurements obtained from temporary or permanently installed sensors. Often, the systems of interest are geometrically large and/or technically complex, which complicates the development and application of physics-based methods. It follows that AI is seen as a key potential contributor enabling SHM in field applications for data-driven analysis. As with many research endeavors, many concepts using AI for SHM have been explored in the literature. Nevertheless, very few AI methods have been deployed in the context of SHM, which may be due to the lack of available data supporting their capabilities, limited integrated AI-SHM systems capable of providing results to users and operators with decision-making capabilities, or certification of AI methods for safety-critical applications. The objective of this Roadmap publication is to discuss the integration of AI at the system level enabling SHM, including associated challenges and opportunities such as those found in common metrics of concern (e.g. transparency, interpretability, explainability, security, certifiability, etc), with a particular focus on providing a path to research and development efforts that could yield impactful field applications. The overview of available methods and directions will provide the readers with applicability of AI for certain SHM designs (software), availability of common data sets for further AI comparisons (data), and lessons learned in implementation (hardware).

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R² values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R² = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

With flooding and other weather events intensifying, more cost-effective erosion and flood control systems are needed. Vetiver (Chrysopogon zizanioides (L.) Roberty), is part of an arsenal of sustainable, low cost, and green infrastructure tools to reduce the risks of erosion, landslides, and flooding. This study investigates vetiver and its broader application to transportation planning. Based on a literature review and interviews with experts, vetiver as a green infrastructure tool is summarized. An evaluation framework was devised in which the plant’s effectiveness to stabilize hillsides and manage stormwater is investigated. This framework is applied to a recent highway flooding case where vetiver could have been used. While site-specific conditions and roadway requirements are critical to its effectiveness as a mitigation tool, additional pathways to understanding, acceptance, and use of vetiver to support transportation resilience requires convergence in engineering, design, and planning disciplines. Understanding barriers to the adoption of vetiver will also support efforts to increase other green infrastructure tools in transportation planning. Improvements in policies, standards, guidance and training and education on vetiver and green infrastructure will support the mitigation of transportation disruptions and community resilience.