[article-cris] Insinööritieteiden korkeakoulu / ENG

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  • Tension capacity of reinforced K-joint connecting rectangular tubes
    (2026-02) Saremi, Pooya; Lu, Wei; Puttonen, Jari
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The research deals with tension capacity of an axially loaded K-joint, which is isolated from a truss-floor where it connects rectangular tubular braces to plate chord and division plate. The study comprises experimental and numerical parts. Experiments were for validating the finite element (FE) modelling used for analyzing with 1080 FE models the sensitivity of joint tension capacity to dimensions of its members and a brace inclination angle. The determining criterion for tension failure was 5 % averaged equivalent plastic strain (EPS) within a control volume (CV) that included brace side walls along welds. An experimentally validated width of CV was 0.2 times brace wall thickness. Among 1080 FE models, brace tension failure occurred if ratio of thickness between a brace wall and chord plate is smaller than 0.5 and a division plate is at least twice thicker than the brace wall. The brace and division plate should be wider than 25 % and 35 % of chord width, respectively. These ratios guide to select braces and a division plate appropriately for a truss chord. Braces angled at 45◦ to chord gave the smallest tension capacity, and an angle of 30◦ led to a greater capacity than the 60◦ angle. Tension capacities were compared to the nominal joint capacities derived from the new Eurocode 1993-1-8:2024. On average, numerical results were 20 % greater than code-based capacities, but in some cases, the code gave capacities that were 93 % of numerical values. Modifications made for the Eurocode equations kept code-based capacities conservative regarding the FE results.
  • Robot-based bridge indirect monitoring leveraging road data filtering for modal frequency estimation
    (2025) Luleci, Furkan; Algadi, Abdulrrahman; Li, Zhenkun; Catbas, F. Necati
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Developments in indirect monitoring of bridges demonstrated practical solutions for network-level life-cycle bridge assessment and evaluation. Utilizing mobile robots is a new area for indirect monitoring, offering scalable approaches to data collection and assessment. This study investigates and experimentally validates a drive-by monitoring methodology using a four-wheeled robot equipped with a sensing system. The methodology employs a simple yet practical and effective approach to isolate the bridge vibration response from interfering factors (road roughness, vehicle dynamics, noise) by applying frequency-domain filtering using data collected from the adjacent roadway. Using the methodology, the study explores the effect of varying driving speeds, drive-stop scenarios, and the robot’s trajectory on different sides over the bridge to determine optimal conditions for precise bridge mode (frequency) identification. Nine experiments conducted on a real-world bridge under jumping excitation demonstrate the identification of up to six modes, with an average variation of 2% compared to reference monitoring data. Driving trajectories showed minimal impact on results, though runs on all sides suggest comprehensive identification. Robot’s dual-capability for real-time mode identification and visual analysis is a promising approach for local assessments in bridge networks, complementing large-scale monitoring by Connected Vehicles in a multi-tiered reliability framework.
  • Surrogate modeling for rapid estimation of spatially-resolved flood damage: Application to a coastal region
    (2026-02) Li, Shicheng; Ding, Can; Yang, James
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Flood damage assessment (FDA) is essential for minimizing economic losses and safeguarding communities. Conventional hydraulic model-based FDA approaches are computationally costly, limiting their practicality for real-time emergency response. Therefore, this study introduces BayFlood, a Bayesian-optimized machine learning surrogate model that enables rapid, accurate, and spatially resolved flood damage estimation using only river discharge and tidal level inputs. The framework is trained and validated on a comprehensive dataset of flood events generated from two-dimensional hydraulic simulations of a coastal basin, covering river flow-dominant, storm surge-dominant, and compound flood scenarios. Among multiple learning engines tested, the boosting-ensemble-driven BayFlood achieves the best performance (coefficient of determination = 0.92–0.98; root mean square error = 4–8 %); the model reduces computational time by two orders of magnitude compared with hydraulic modeling, generating damage results within minutes. Monte Carlo uncertainty analysis (1000 runs, 5 % noise level) reveals a mean damage-rate uncertainty of 18 %, confirming model robustness. By effectively combining forecasting efficiency, accuracy, and spatial damage mapping, the BayFlood provides a practical and scalable tool for pre-disaster planning, real-time emergency response, and post-disaster recovery.
  • State-of-the-art review of vibration-based bridge health monitoring using Artificial Intelligence
    (2025) Li, Zhenkun; Feng, Kun; Markou, Athanasios; Lin, Weiwei
    A4 Artikkeli konferenssijulkaisussa
    Due to the deterioration and aging of bridge structures over the past decades, structural health monitoring (SHM) systems have garnered significant attention from researchers worldwide. SHM systems encompass multiple modules, including sensing, data collection, transmission, management, damage detection, and safety assessment. As a highly interdisciplinary field, SHM integrates various technologies such as sensor sensing, data acquisition, signal processing, and optimization. One of the promising approaches in bridge health monitoring (BHM) is vibration-based monitoring, which provides critical information for bridge condition assessment and maintenance. In recent years, advancements in computer hardware and Artificial Intelligence (AI) algorithms have significantly enhanced the capability of vibration-based BHM systems. AI, with its advanced analytical power and high sensitivity to anomalies, has been widely adopted in these applications, enabling more efficient and accurate damage detection. This paper presents a state-of-the-art review of vibration-based BHM using various AI techniques over the past two years. It explores how AI can facilitate data-driven BHM systems for bridges and discusses key aspects of the BHM process, including existing methodologies and current challenges. Additionally, the paper highlights potential research directions to guide future studies, offering insights and opportunities for researchers in the field.
  • Parameter Optimization for Climate-Resilient IEQ Assessment : Validating Essential Metrics in the PICSOU Framework Across Divergent Climate Zones
    (2026-01) Jiang, Qidi; Liu, Cheng; Wang, Chunjian; Chen, Zhiyang; Salonen, Heidi; Kurnitski, Jarek
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    To enhance the climate adaptability and diagnostic precision of university sustainability frameworks, this study presents a critical advancement to the PICSOU (Performance Indicators for Core Sustainability Objectives of Universities) framework’s Indoor Environmental Quality (IEQ) module. The research employs a comparative approach across two distinct climate zones: the campus of Chengdu Jincheng College in a humid subtropical climate (CDJCC; Köppen Cwa) with natural ventilation, and the campus of Tallinn University of Technology in a temperate climate (TalTech; Köppen Dfb) with mechanical ventilation. A key innovation at CDJCC was the deployment of a novel, integrated sensor that combines a Frequency-Modulated Continuous Wave (FMCW) radar module for real-time occupancy detection with standard IEQ sensor suite (CO2, PM2.5, temperature, humidity), enabling unprecedented analysis of occupant-IEQ dynamics. At TalTech, comprehensive IEQ monitoring was conducted using standard sensors. Results demonstrated that mechanical ventilation (TalTech) effectively decouples indoor conditions from external fluctuations. In contrast, natural ventilation (CDJCC) exhibits strong seasonal coupling, reflected by a Seasonal Ventilation Efficacy Coefficient ((Formula presented.)), indicating that seasonal differences in effective ventilation are present but vary by indoor space type under occupied conditions. Consistent with this stronger indoor–outdoor linkage, PM2.5 infiltration was also pronounced in naturally ventilated spaces, as evidenced by a high infiltration factor ((Formula presented.) ratio) that remained consistently elevated. This work conclusively validates a conditional, climate-resilient workflow for PICSOU’s IEQ category, integrating these empirical coefficients to transform its IEQ assessment into a dynamic and actionable tool for optimizing campus sustainability strategies globally.
  • Plastic waste-based Cu-BDC and COOH-MWCNT@Cu-BDC MOF for enhancing PPSU membrane for efficient heavy metal removal from industrial wastewater
    (2026-02) Jaid, Ghaidaa M.; AbdulRazak, Adnan A.; al-Shaeli, Muayad; Alsalhy, Qusay F.; Al-Juboori, Raed
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The rapid expansion of the battery industry, driven by technological advancements and increasing demand for energy storage, presents a significant environmental threat due to the release of heavy metals. This study reports on fabricating a newfangled composite membrane for lead removal as a model contaminant. The new membrane is composed of polyphenylsulfone (PPSU) incorporated with copper-based metal-organic frameworks (Cu-BDC MOFs) and carboxyl-functionalized multi-walled carbon nanotubes (COOH-MWCNT@Cu-BDC). The MOFs were synthesized via upcycling PET plastic waste applying hydrothermal methods. Two types of membranes were developed with varying filler concentrations of 0.05–0.2 wt%, with optimal result at 0.1 wt%. Modified membranes showed improved hydrophilicity, porosity, and mechanical strength. The optimum membrane (PPSU/COOH-MWCNT@Cu-BDC) exhibited superior pure water flux (61 L/m²·h) and Pb²⁺ rejection (99.7 %) compared to pristine membrane (16.25 L/m²·h, 89.3 %). The optimum membrane had high flux recovery ratio (93.5 %), attributed to improved surface morphology and hydration layer formation. With such characteristics, the membrane maintained stable performance (31.25 L/m²·h, 94.15 % Pb²⁺ rejection) in real battery wastewater over 72 h. The study highlights the synergistic effects of Cu-BDC and COOH-MWCNTs in enhancing membrane performance, offering a sustainable and efficient solution for heavy metal removal from wastewater.
  • An integrated framework for fully sampled vehicle trajectory reconstruction using a fused dataset
    (2025-01-09) Ma, Jingfeng; Roncoli, Claudio; Ren, Gang; Yang, Yuanxiang; Wang, Shunchao; Yu, Jingcai; Wang, Bingtong
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Vehicle trajectories offer valuable insights for understanding traffic dynamics and optimising traffic control. However, the collection of fully-sampled vehicle trajectories is challenging due to unaffordable costs. To maximise the utility of sparse and limited trajectories, this study tailors an integrated framework for fully-sampled vehicle trajectory reconstruction. The proposed framework elaborates on a three-step work. Firstly, a piecewise cubic Hermite interpolating polynomial (PCHIP) is employed to reconstruct individual probe vehicle (PV) trajectories, and a piecewise order-changing model is proposed to capture overtaking dynamics. Secondly, a speed contour map is constructed to provide speed baselines for estimating undetected non-probe vehicle (NPV) trajectories on a region-by-region basis. Two candidate trajectories are estimated by conducting car-following (CF) model and inverse car-following (ICF) model, respectively. Thirdly, a weighted fusion model is designed to estimate NPV trajectories by integrating the model predictive control (MPC) algorithm. Comparative analysis proves that the combined model performs better than the pure CF model.
  • Vehicle Trajectory Reconstruction from not working Sparse Data Using a Hybrid Approach
    (2025-02-01) Ma, Jingfeng; Roncoli, Claudio; Ren, Gang; Yang, Yuanxiang; Cao, Qi; Deng, Yue; Li, Jingzhi
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Vehicle trajectories deliver precious information, supporting traffic state estimation and congested traffic mitigation. However, collecting fully sampled vehicle trajectories is difficult due to unaffordable data-collection costs and maintenance costs of data collection equipment. This study aims to accurately reconstruct missing vehicle trajectories by proposing a novel approach based on sparse data collected from different types of urban roads. First, an improved map-matching algorithm combining a hidden Markov model (HMM) and a bidirectional Dijkstra algorithm is proposed to ensure the high quality of the input data for trajectory reconstruction. The matched trajectory points are then converted into a two-dimensional time-space map. Subsequently, a piecewise cubic Hermite interpolating polynomial (PCHIP) algorithm is developed to reconstruct vehicle trajectories based on a total of 371 taxi trajectories on three types of urban roads. The results demonstrate that the speed-based mean relative error (MRE) value is less than 9%, and the speed-based root mean square error (RMSE_v) value is less than 6 km=h. Furthermore, the location-based MAE is found to be less than 5.86 m, and the location-based RMSE_x value is less than 7 m. Additionally, a model comparison is conducted, and the outcomes evidence that the combined method performs better than state-of-the-art approaches.
  • Feasibility of machine learning application in pavement life cycle assessment : A review
    (2026-05) Das, Bhaskar Pratim; Deka, Shankar; Dettenborn, Taavi; Bordoloi, Sanandam
    A2 Katsausartikkeli tieteellisessä aikakauslehdessä
    The need to mitigate the environmental impacts of pavement systems has increased interest in life cycle assessment (LCA), but its implementation often faces challenges, such as data uncertainties, inconsistent impact methods, and limited decision-support capabilities. This review explores the utilization of machine learning (ML) to address these challenges and enhance LCA workflows. This review addresses the current practices and challenges in pavement LCA by structuring it around its four phases, i.e., goal and scope definition, inventory analysis, impact assessment, and interpretation. Diverse applications of data-driven ML techniques in pavement systems and LCA are highlighted. Review indicates that ML can enhance pavement LCA by predicting context-specific inventory data, clustering diverse datasets to detect inconsistencies, and simulating different allocation scenarios. Moreover, multiple impact categories forecasting seems possible with ML-based inventory analysis. ML-based visualisations, such as decision trees, can clarify variables’ contributions to environmental outcomes. ML can also support sensitivity and uncertainty analyses to strengthen decision-making.
  • Optimizing Urban Traffic Networks With Dynamic Saturation Rates in a Mixed Autonomy Environment
    (2025-06-27) Haris, Muhammad; Roncoli, Claudio
    A4 Artikkeli konferenssijulkaisussa
    This work presents a novel optimization-based control framework for managing traffic flow in urban networks with mixed autonomy, where Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs) coexist. The proposed approach extends the store-and-forward model by incorporating a dynamic saturation flow rate that reflects the level of autonomy in vehicle queues. The problem is formulated as a Non-convex Quadratic Program (NQP), capturing the dynamics of queue lengths, spillback effects, green time allocation, CAV routing, and variable saturation flow rates. To solve the NQP efficiently, we reformulate bilinear terms using under- and over estimators, transforming the non-convex problem into a series of convex subproblems—specifically, a Mixed-Integer Quadratic Program (MIQP)—which is further converted into a Mixed-Integer Linear Program (MILP) by linearizing quadratic terms in the objective function. This approach significantly reduces computational complexity while enabling potential real-time implementation. Numerical simulations on a grid network demonstrate the effectiveness and efficiency of the proposed methodology.
  • Multispectral airborne laser scanning for tree species classification : A benchmark of machine learning and deep learning algorithms
    (2026-03) Taher, Josef; Hyyppä, Eric; Hyyppä, Matti; Salolahti, Klaara; Yu, Xiaowei; Matikainen, Leena; Kukko, Antero; Lehtomäki, Matti; Kaartinen, Harri; Thurachen, Sopitta; Litkey, Paula; Luoma, Ville; Holopainen, Markus; Kong, Gefei; Fan, Hongchao; Rönnholm, Petri; Vaaja, Matti; Polvivaara, Antti; Junttila, Samuli; Vastaranta, Mikko; Puliti, Stefano; Astrup, Rasmus; Kostensalo, Joel; Myllymäki, Mari; Kulicki, Maksymilian; Stereńczak, Krzysztof; Pires, Raul de Paula; Valbuena, Ruben; Carbonell-Rivera, Juan Pedro; Torralba, Jesús; Chen, Yi-Chen; Winiwarter, Lukas; Hollaus, Markus; Mandlburger, Gottfried; Takhtkeshha, Narges; Remondino, Fabio; Lisiewicz, Maciej; Kraszewski, Bartłomiej; Liang, Xinlian; Chen, Jianchang; Ahokas, Eero; Karila, Kirsi; Vezeteu, Eugeniu; Manninen, Petri; Näsi, Roope; Hyyti, Heikki; Pyykkönen, Siiri; Hu, Peilun; Hyyppä, Juha
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing, but challenges remain in leveraging deep learning techniques and identifying rare tree species in class-imbalanced datasets. This study addresses these gaps by conducting a comprehensive benchmark of deep learning and traditional shallow machine learning methods for tree species classification. For the study, we collected high-density multispectral ALS data (>1000 pts/m2) at three wavelengths using the FGI-developed HeliALS system, complemented by existing Optech Titan data (35 pts/m2), to evaluate the species classification accuracy of various algorithms in a peri-urban study area located in southern Finland. We established a field reference dataset of 6326 segments across nine species using a newly developed browser-based crowdsourcing tool, which facilitated efficient data annotation. The ALS data, including a training dataset of 1065 segments, was shared with the scientific community to foster collaborative research and diverse algorithmic contributions. Based on 5261 test segments, our findings demonstrate that point-based deep learning methods, particularly a point transformer model, outperformed traditional machine learning and image-based deep learning approaches on high-density multispectral point clouds. For the high-density ALS dataset, a point transformer model provided the best performance reaching an overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065 segments and 92.0% (85.1%) with a larger training set of 5000 segments. With 1065 training segments, the best image-based deep learning method, DetailView, reached an overall (macro-average) accuracy of 84.3% (63.9%), whereas a shallow random forest (RF) classifier achieved an overall (macro-average) accuracy of 83.2% (61.3%). For the sparser ALS dataset, an RF model topped the list with an overall (macro-average) accuracy of 79.9% (57.6%), closely followed by the point transformer at 79.6% (56.0%). Importantly, the overall classification accuracy of the point transformer model on the HeliALS data increased from 73.0% with no spectral information to 84.7% with single-channel reflectance, and to 87.9% with spectral information of all the three channels. Furthermore, we studied the scaling of the classification accuracy as a function of point density and training set size using 5-fold cross-validation of our dataset. Based on our findings, multispectral information is especially beneficial for sparse point clouds with 1–50 pts/m2. Furthermore, we observed that the classification error follows a power law ɛ(m)≈m−α as a function of the training set size m, and the classification error of the point transformer reduced significantly faster with increasing training set size compared to RF.
  • Parameter identification using transfer learning and influence functions : The case of modeling lithium-ion battery
    (2025-04-01) Ping, Xiaojing; Luan, Xiaoli; Yu, Wei; Mei, Peng; Liu, Fei
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Parameter identification through transfer learning utilizes pre-identified source models to improve the identification performance of the target system, especially under challenging conditions such as large noise or the presence of outliers. This paper focuses on the problem of source selection for the multi-source transfer scenario without repeated identification procedures. Employing influence functions can reliably quantify the effect of different sources on the model accuracy of the target system. This enables the selection of the optimal source model from all candidates, thereby maximizing the target model's performance. The proposed approach is validated through a numerical case study and an application involving a equivalent circuit model of lithium-ion batteries in electric vehicles, demonstrating its effectiveness and robustness.
  • Suokasvillisuuden havainnoinnista lajikohtaisilla hyperspektriaineistoilla
    (2025-12-29) Salko, Sini-Selina
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Seloste artikkelista Salko, S.-S., Hovi, A., Burdun, I., Juola, J., Karlqvist, S., Rautiainen, M. 2025. Assessing Species Fractional Cover and α-diversity in Boreal Peatlands Across Trophic Levels Using Hyperspectral Data. Ecology and Evolution 15(8), e71941. https://doi.org/10.1002/ece3.71941.
  • Assessing blockchain technology's technical utility in construction supply chains : A multi-KPI decision support approach via use cases
    (2026-02) Olawumi, Timothy O.; Ojo, Stephen; Muftaudeen, Saheed Toyin; Odeh, Acheme Okolobia; Amoo, Taiwo
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Blockchain technology (BCT) holds significant potential to transform construction supply chains (CSCs) by addressing longstanding challenges related to transparency, efficiency, and traceability. This study investigates and develops a rigorous, KPI-centric framework that systematically maps blockchain’s enabling capabilities (ECs) to key performance indicators (KPIs) critical to CSC performance. Through a hybrid methodology combining content analysis and design science research (DSR), the paper introduces a web-based Decision Support Tool (DST) to guide stakeholders in evaluating the technical suitability of blockchain for construction projects. The DST operates in two phases: first, assessing blockchain applicability through a structured diagnostic; second, recommending ‘best-fit’ blockchain stacks by aligning selected KPIs with relevant use cases and ECs. Validation via simulated case scenarios demonstrates the DST’s robustness in supporting early-stage, technically grounded decision-making and recommends blockchain solutions tailored to user-defined KPIs and use cases. The findings reveal that BCT, through automation, immutable data sharing, decentralized governance, and the like, can significantly improve CSCs' performance. By bridging the gap between conceptual promise and practical application, this research provides both theoretical advancements and actionable insights for digital transformation in the construction industry. It contributes a replicable decision-support architecture for technology adoption and performance optimization in complex, multi-stakeholder supply chain environments.
  • Design, performance assessment, and machine learning-driven optimization of a novel low-carbon urban waste-to-x polygeneration system : multi-scenario analysis of hydrogen and methane production
    (2026-02-01) Babaei Khuyinrud, Mohammadreza; Shokri Kalan, Ali; Pourtalebi, Borhan; Ahamdi, Mehran; Jangi, Iraj; Lü, Xiaoshu; Yuan, Yanping; Rosen, Marc A.
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Growing energy demand, waste accumulation, and greenhouse gas emissions necessitate integrated, low-carbon energy options. This study proposes a novel waste-to-x polygeneration system uniquely integrating biomass gasification with gas turbine, supercritical CO2, Kalina, organic Rankine, and steam Rankine cycles, coupled with advanced wastewater treatment, carbon capture, a proton exchange membrane (PEM) electrolysis, and methanation. The system simultaneously produces electricity, district heat, oxygen, hydrogen, and methane, advancing beyond typical waste-to-energy approaches by combining multi-vector fuel production with near-zero emissions. Under baseline operation, the system attains overall energy and exergy efficiencies of 35.0 % and 39.9 %, delivering 3510 kW net power and 1310 kW heating, and daily outputs of 131.6 kg hydrogen, 2106 kg oxygen, and 296.3 kg methane, while capturing 87 % of CO2 emissions (177.7 t/day) and treating 116.6 t/day wastewater. Exergy analysis identifies the biomass gasifier as the primary exergy destruction source (8014 kW), whereas mixers and splitters achieve the highest exergy efficiencies (>99.0 %). Employing a machine-learning-assisted multi-objective grey wolf optimizer (MOGWO), for dual fuel production scenario, enhances energy and exergy efficiencies to 49.5 % and 53.6 %, respectively; boosts hydrogen, oxygen, and methane production by 23.0 %; reduces net power by 6.9 %; and increases heating output by up to 29.1 %. Among fuel-production modes at the optimum, the hydrogen-only case achieves the highest efficiencies (49.7 % energy, 53.6 % exergy). This integrated approach offers a comprehensive and flexible option for sustainable urban resource management.
  • A comprehensive review of indirect bridge health monitoring
    (2026-03-01) Li, Zhenkun; Lin, Weiwei; Kim, Chul-Woo; Limongelli, Maria Pina; Chatzi, Eleni
    A2 Katsausartikkeli tieteellisessä aikakauslehdessä
    Indirect Bridge Health Monitoring (BHM) using indirect measurements of the response from passing vehicles has recently gained significant attention from researchers within the Structural Health Monitoring (SHM) domain. This approach requires only one or a few sensors installed on the vehicle, making it more cost-effective, efficient, and easier to implement than traditional methods, which demand numerous sensors on bridges. Recent advancements in both algorithms and hardware have further accelerated progress in this field. This paper aims to provide a comprehensive, one-stop review of indirect BHM using measured vehicle response since 2004. It systematically analyzes the connections and integrations within existing literature, incorporating rapidly emerging state-of-the-art studies. The review initiates with a bibliometric analysis, covering annual publication trends, keyword cooccurrence, and authorship networks, followed by a discussion on the fundamental theories of vehicle–bridge interaction. Subsequently, it summarizes the vehicle, bridge, and road roughness models used in indirect BHM. Furthermore, it explores current techniques and challenges in identifying bridge modal parameters, such as bridge frequencies, mode shapes, and damping ratios, as well as in indirect bridge damage detection using signal processing, modal-based, and data-driven methods. Additionally, this review includes affiliated studies that, while not directly related, contribute to the advancement of indirect BHM. Finally, recent developments in 2025, future investigation directions, and key conclusions are provided. It is intended to serve as a fundamental resource for researchers seeking to advance their studies in the field of indirect BHM.
  • Integrating more-than-human approaches in urban planning pedagogy : A case study from Europe
    (2026-02) Eräranta, Susa; Czarnecka, Adrianna; Piotrkowska, Monika; Hytönen, Jonne
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Despite decades of sustainability efforts, urban planning often leads to environmental degradation. This paper questions the anthropocentric assumptions in planning education and advocates for integrating nonhuman perspectives through a more-than-human approach. To embrace the intrinsic value and agency of all actors, the paper approaches more-than-human planning education through three stages: recognition, inclusion, and co-flourishing. As traditional planning curricula prioritize human needs, overlooking the interconnectedness of all life, the paper provides already existing examples of more-than-human planning education by reflecting on the experiences of students and course staff on two Master's level studio courses at Warsaw University of Technology (Poland) and Aalto University (Finland) to learn from the potential of more-than-human education. These courses aimed to strengthen planners' roles in sustainability transformations by recognizing the agency and intrinsic value of nonhumans. The findings suggest that the core aim of planning from a more-than-human perspective is in protecting the planet’s life-supporting systems – not only in protecting individual humans, plants, or animals. This requires a shift from individually focused thinking towards more holistic systemic approaches. Based on this, the paper initiates a discussion on the need for a paradigm shift in planning education to embrace more-than-human perspectives.
  • Mapping world's coastal population facing water-related risks
    (2026-02) Varis, Olli; Taka, Maija; Kummu, Matti
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Endowed with opportunities from both land and ocean, coastal areas attract expanding human populations and economic activities. At the same time, they face growing societal and environmental pressures from both the above river catchments and the bordering sea due to climate change, ecosystem degradation, and expansion of built-up areas. Despite the accumulation of human population, economic activities, and environmental impacts, we lack social-ecological systems analysis on water-related risks to world's coastal human population. To address this research gap, we analyze the spatial extent of six globally important water stressors to people within the world's coastal zone (100 km from the coastal line) and classify this zone globally into 12 groups by distance from the coastline and elevation from the mean sea level. Adopting the approaches of the UN Sendai Framework and IPCC, we produce risk maps from the stressor maps by multiplying them with population exposure and vulnerability. For most risks, geographical hotspots are the Chinese coast, Bay of Bengal, Gujarat, and the Island of Java. The analysis reveals fundamental differences between water stressors and related risks, often mixed in scholarly literature. Both manifest specific geographic patterns and latitudinal profiles. Our study highlights the importance of high-resolution spatial analysis of vulnerability, exposure, and risks posed by water related stressors in the world's coastal zone, in a manner prompted by key policy bodies to promote policy design and shared responsibility for managing stress-prone areas.
  • Cooling design day generation methods' and risk levels' impact on the design capacities and risk of thermal discomfort in a cold climate
    (2026-02-01) Seyed Salehi, Seyed Shahabaldin; Kurnitski, Jarek; Thalfeldt, Martin
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Meteorological data is needed to determine the size of the cooling/heating systems. A design day is commonly used for cooling. ISO 15927-2 and the ASHRAE handbook provide comprehensive guidelines for generating design day weather data with different risk levels. However, the impact of different risk levels and design day generation methods on thermal discomfort has been rarely quantified. This study integrated design day analysis with long-term thermal comfort simulations to assess the cooling system design capacities obtained using design days generated by the aforementioned methods and different risk levels in the temperate and mild climate of Estonia. A generic open-plan office was used for sizing cooling units and subsequently for thermal comfort assessment based on long-term historical data. The ASHRAE Fundamentals method performed better in Estonia's climate, whereas ISO 15927-2 needs further development to avoid reaching controversial results among risk levels. Solar radiation and internal heat gains contributed most to the space cooling capacity, and the differences between the design capacities for the risk levels were negligible. The space cooling temperature setpoint of 25 °C was rarely or never exceeded by more than 0.5 °C in long-term simulations, except in the North-oriented zones. Therefore, a risk level of 10 % was recommended for sizing space cooling without significantly increasing the risk of thermal discomfort. Ventilation supply air cooling capacity depends more on the enthalpy of outdoor air, and therefore, a risk level of 5 % is recommended.
  • Modeling axisymmetric contact problems within strain gradient elasticity
    (2026-03) Schek, Lucca; Morozov, Aleksandr; Khakalo, Sergei; Müller, Wolfgang H.
    A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Experimental testings indicate that the effective hardness of materials, as measured in normal contacts, depends on the size of the indenter. While this effect cannot be described by classical continuum theories, such a size-dependence can be modeled with generalized continuum theories. In this study, the application of simplified strain gradient elasticity in describing frictionless normal contacts is investigated. Axisymmetric contact problems for indenters of different shape are modeled within the framework of simplified strain gradient elasticity using a rigid body penalty-based contact approach in isogeometric analysis. The numerical implementation is verified using an existing semi-analytical solution for strain gradient elasticity. This study reveals significant deviations from classical theory, particularly in the form of the pressure distribution under the indenters. It is shown that the stress singularity present in the classical solution to the indentation test of a flat cylinder vanishes in the case of strain gradient elasticity. Furthermore, the importance of gradient elasticity for describing scale effects of normal contacts with indenters is demonstrated.