A new open-source desktop application called FAT-SM promises to close a stubborn gap in structural engineering: the distance between the fatigue equations engineers have used for decades and the torrent of live sensor data flowing from real structures. Developed by Carlos Souto, Bruno Pedrosa and José Correia and described in the journal SoftwareX, the tool takes raw stress or strain time series from structural health monitoring systems and converts them into actionable fatigue metrics, including accumulated damage and remaining service life, within a single integrated platform.
The problem FAT-SM tackles is one of the most consequential in civil and marine engineering. Port, offshore and marine structures are continuously subjected to complex cyclic loading induced by waves, wind and operational conditions, leading to progressive fatigue damage accumulation. Fatigue is widely recognized as one of the primary causes of structural degradation in metallic structures, particularly in welded joints and critical components, significantly affecting reliability and lifecycle performance. The scientific challenge, the authors note, lies in accurately quantifying fatigue damage under realistic, stochastic loading conditions and predicting the remaining service life of structures operating in harsh environments.
Conventional fatigue assessment relies on well-established but simplified machinery: stress-life curves, known as S-N curves, combined with cycle counting techniques such as the rainflow counting algorithm and linear damage accumulation models like the Palmgren-Miner rule. While these methods are well established, they rely on simplified assumptions, including linear damage accumulation and the neglect of load sequence effects, which can lead to inaccuracies under variable amplitude loading. Furthermore, such approaches are generally implemented in offline analyses, limiting their applicability in continuous monitoring and real-time decision-making. In other words, the standard toolkit was built for the design office, not for a structure that is already in the water and reporting its own condition every second.
Recent research has emphasized the importance of integrating fatigue assessment with experimental and monitoring data to improve prediction accuracy. In particular, a study conducted by Souto and colleagues demonstrates that fatigue damage estimation based on measured strain data provides more realistic results compared to purely design-based approaches. That finding highlights the need for computational tools capable of processing real-time structural response data and incorporating it into fatigue assessment workflows. Advances in structural health monitoring have enabled continuous acquisition of structural response data through sensor networks, allowing for condition-based assessment of structural integrity, while the concept of the digital twin has emerged as a promising framework for integrating monitoring data with numerical models to support real-time analysis and predictive maintenance.
Despite these developments, existing solutions remain fragmented, often requiring multiple tools to perform data processing, fatigue analysis and life prediction, which limits their usability in engineering practice. Commercial and research tools for fatigue analysis, including ANSYS nCode DesignLife and FE-SAFE, are primarily oriented toward design-phase simulations. Research-oriented tools such as WAFO provide capabilities for stochastic fatigue analysis but are not typically integrated with real-time monitoring systems. FAT-SM differentiates itself by combining fatigue assessment, life prediction and monitoring integration within a single, user-oriented platform tailored for operational conditions.
The software is implemented as a modular desktop application developed in C++ using the Qt framework, following a clear separation between the graphical user interface and the core computational model. It adopts a lightweight Model-View design pattern, where fatigue analysis logic is encapsulated in domain-specific classes and exposed to the user through interactive GUI components. The Model performs automatic validation and consistency checks before updating its state, notifies the View of changes through Qt’s signal-slot mechanism, and acts as a single source of truth, maintaining synchronization between the computational core and what the user sees on screen. The current code version, v2026.05.26, is released under the GPLv3 license, with a reproducible capsule available on GitHub; it is compiled for Windows x64 using MSVC v143 build tools and Qt 6.9.0, though compilation is possible for any Qt-supported system.
Architecturally, the Model is structured around a Project class that manages multiple constructional details and enables multi-scenario analyses within a single engineering project. Each detail derives from an abstract Detail class that defines a unified interface for fatigue analysis, with two implementations currently provided: SnDetail and LefmDetail. The SnDetail implements a stress-life assessment workflow comprising stress-history processing, rainflow cycle counting and cumulative damage calculation using the Palmgren-Miner rule, in which the number of cycles experienced at each stress level is compared against the corresponding number of cycles to failure from the S-N curve; if the total accumulated damage reaches one, fatigue failure is expected. The LefmDetail implements a damage-tolerant workflow based on linear-elastic fracture mechanics, comprising stress-history processing, rainflow cycle counting, stress intensity factor calculation and incremental crack-growth evaluation using Paris’ law, where the crack increment associated with each counted stress bin depends on material constants, the stress range, the current crack length and a geometry correction factor defined for the selected standardized constructional detail. Crack growth below a threshold is ignored, and the counted loading history is repeatedly applied until the crack reaches a limiting size, the critical stress intensity factor range is reached, or the maximum considered service life is attained.
The workflow for users follows a deliberate sequence. For an S-N detail, the user first defines the S-N curve through the detail category, meaning the fatigue strength at a reference of two million load cycles, along with any number of slope parameters and corresponding endurance limits, ensuring compatibility with standards such as EN 1993-1-9 while also allowing user-defined extrapolations. The stress-time history is then specified in tabular format, including a repetition parameter for cyclic loading, with automatic detection of local extrema, or load reversals, and interactive plots of the input data. Rainflow cycle counting is implemented according to the ASTM E1049-85 standard, with efficient execution in C++ enabling fast processing of large datasets; a reproducible verification example comparing the FAT-SM implementation with MATLAB’s proprietary Signal Processing Toolbox is provided in the GitHub repository. Results are visualized through one-dimensional histograms of stress range and mean stress, each divided into ten bins, and a two-dimensional histogram representing the joint distribution of stress range and mean stress on a ten-by-ten bin grid, all fully interactive.
After the cycle counts are in hand, fatigue damage accumulation is evaluated using Miner’s rule, with the application of safety factors according to EN 1993-1-9 or user-defined values, including detailed cycle contributions and identification of failure conditions. If the accumulated damage exceeds unity, fatigue failure is predicted and the result is visually highlighted in red; otherwise, the component is considered to remain within the specified safety limits. For LEFM-based details, the user selects a constructional detail from a list of predefined and standardized configurations currently based on IIW recommendations, defines the fatigue crack growth behaviour through the material parameters of the Paris-Erdogan equation, and then processes the stress-time history with the same rainflow counting algorithm. The remaining fatigue life is estimated by numerically integrating the Paris-Erdogan equation from an initial crack size to a pre-defined failure criterion, performed for a range of initial defect sizes to compute a fatigue design curve that relates detected crack size to the corresponding remaining fatigue life, thus supporting condition-based maintenance decisions.
The authors position the software’s impact on two fronts. From a scientific perspective, FAT-SM opens research questions related to the real-time interaction between measured structural responses and fatigue damage evolution, enabling investigation of how non-stationary loading and operational variability influence cumulative damage, moving beyond classical assumptions of linear damage accumulation. The availability of a unified platform also enables research into hybrid fatigue models combining physics-based and data-driven components, adaptive prognostic algorithms capable of updating predictions based on incoming monitoring data, and uncertainty quantification in fatigue life prediction under operational conditions, an area the authors describe as remaining largely open in current literature. In terms of daily engineering practice, the tool contributes to a shift from periodic, inspection-based maintenance strategies toward continuous, condition-based fatigue assessment, particularly in safety-critical infrastructure such as offshore wind turbines, cranes and marine structures. Engineers can directly process sensor data, such as strain or acceleration time series, to obtain fatigue damage indicators and remaining useful life estimates, supporting faster decision-making and more objective maintenance planning. The authors report that FAT-SM has been adopted in academic and applied research contexts, used as a computational basis in fatigue monitoring studies involving offshore structural components and experimental SHM setups, and shared within research collaborations for processing long-term monitoring datasets, although it remains primarily used in research environments and has not yet been commercialized as a standalone product or spin-off company. Its modular structure, they argue, facilitates integration into larger SHM platforms and decision-support systems, with a clear pathway toward industrial uptake in digital twin frameworks for infrastructure monitoring, where tools like FAT-SM could serve as core fatigue assessment modules within larger asset management ecosystems.
Subject of Research: An open-source software tool that transforms structural monitoring data into fatigue damage and remaining life predictions
Article Title: FAT-SM: Fatigue assessment tool for structural monitoring
Article References: Souto, C., Pedrosa, B., & Correia, J. (2026). FAT-SM: Fatigue assessment tool for structural monitoring. SoftwareX, 36, Article 103028. https://doi.org/10.1016/j.softx.2026.103028
Image Credits: AI Generated
DOI: 10.1016/j.softx.2026.103028
Keywords: fatigue assessment, structural health monitoring, rainflow counting, S-N curves, Palmgren-Miner rule, Paris' law, crack growth, digital twin, offshore structures, open-source software, remaining service life, condition-based maintenance
News Source: Denise Maddox. (November 4, 2026). Open-source software turns raw strain data into real-time fatigue life predictions for monitored structures. Scienmag.
