The utilisation of stiffened fibre-reinforced polymer (FRP) box-beams is highly advantageous for lightweight bridge structures; however, their design is computationally intensive due to anisotropy and buckling sensitivity. This study presents a computationally efficient artificial neural network (ANN) model for predicting the global flange buckling strength of stiffened laminated composite FRP box-beams subjected to lateral loading. A comprehensive database was generated using validated finite element analysis (FEA) considering fibre orientation, stiffener geometry, and orthotropic stiffness effects. Five governing parameters (αsf, CEIw, D1/D2, (EA)fs/(EA)fp, and βsf) derived from laminate theory and stiffened panel mechanics were used as ANN inputs. Among the evaluated models, the Bayesian regularised ANN achieved an average prediction error of 3.01% and a maximum error of 4.77%, while reducing computation time from approximately 300 s (FEA) to less than 0.01 s per prediction. The proposed model provides a reliable surrogate tool for design optimisation of composite box-beams.
This paper addresses the problem of structural collapse by establishing a novel analogy between the ductile-to-brittle transition observed in materials and the progressive failure mechanisms in framed structural systems. This study builds upon existing literature, which often considers fragility indices, stress intensification factors in the presence of cracks, and energy-based failure criteria at the material scale, extending these concepts to analyse the behaviour of full-scale structures under progressively increasing vertical loads. The work focuses on reinforced concrete frames with varying levels of structural hierarchy, specifically 2 x 2, 5 x 5, and 11 x 11 configurations, to explore how hierarchical organisation influences the onset and propagation of damage. The investigation evaluates the ductile-to-brittle transition using a dual-scale approach, combining geometric and energetic perspectives. At the macroscopic level, geometric criteria serve as indicators of structural fragility, identifying points at which load redistribution becomes insufficient and localised damage begins to propagate towards global instability. At the microscale, energetic criteria derived from classical fracture mechanics, including the concept of a critical fracture length based on material properties such as tensile strength, elastic modulus, and fracture energy, provide a physically grounded measure of the structure's ability to dissipate energy before catastrophic failure occurs.
Flood damage is becoming increasingly frequent and diverse because of the effects of climate change and other factors. In Japan, recent flood prevention measures include providing information to residents through risk information maps called 'hazard maps' as 'non-structural measures', thereby ensuring suitable evacuation behaviour and raising awareness. This study developed tools for local government officials to provide residents detailed inundation information for small- and medium-sized rivers. Typhoon Hagibis flooded the Shinano River, causing severe damage to Obuse Town in northern Nagano Prefecture. Focusing on this event, a video was created to link the information on the map to actual evacuation behaviour. Flood inundation simulations were performed using the International River Interface Cooperative (iRIC) software and the Nays2DFlood solver. The terrain model for the simulation was created using a digital elevation model (DEM). In the iRIC simulation, sufficient reproducibility was obtained using a 10 m & times; 10 m mesh, a simple modification of the levee portion of the DEM, and the installation of a drainage pump station. The simulation allowed the adjustment of rainfall intensity and river flow, enabling the prediction of rainfall and flood damage. The new inundation information, including spatiotemporal flood patterns, can be used for evacuation planning.
The Srikakulam district in eastern India frequently experiences water stress due to rainfall variability, inadequate storage facilities, and increasing agricultural and domestic demand. This study aims to identify regionally suitable sites for dams and barrages along the Nagavali and Vamsadhara rivers to support integrated water resources planning in a data-limited semi-arid basin. Remote sensing and Geographic Information System (GIS) techniques were integrated with the Analytical Hierarchy Process (AHP) to assess regional suitability. Sixteen thematic layers derived from Landsat imagery, ASTER DEM, published geological maps, and long-term rainfall data were used to represent topographic, geological, geomorphological, land surface, and hydrological factors. Criteria weights were assigned through expert-based pairwise comparison and validated using consistency ratios. A composite suitability map was produced using a weighted overlay approach. The results classify zones of high, moderate, and low suitability for water-harvesting structures. Validation against the spatial distribution of existing dams and barrages showed strong agreement, confirming the reliability of the proposed screening framework. This research report offers a generalisable and economical GIS–AHP procedure of preliminary screening of potential dam and barrage locations in the data-deficient semi-arid basins, and can be applied in other hydro-climatic environments.
Low-volume rural roads (LVRRs) form the backbone of India’s rural mobility network, yet the sustainability of their maintenance remains largely unaddressed in existing rating systems. This study develops SPATRR – a Sustainable Pavement Assessment Tool for Rural Roads – to evaluate the sustainability performance of maintenance activities using a structured, certifiable, and locally contextual approach. A comprehensive indicator set was constructed through literature review, field observations, and stakeholder consultations. Importance rankings were derived using the relative importance index and reliability validated using Cronbach’s alpha (α > 0.90). Category weights were generated through the analytic hierarchy process (AHP) with expert input from agencies, contractors, practitioners, and road users. The final SPATRR tool includes eight sustainability categories and 41 indicators, supported by an AHP-weighted scoring model and a four-tier sustainability certification scheme. A real-world case study demonstrates the applicability of the tool and highlights improvement pathways. SPATRR offers a simple, transparent, and context-specific assessment mechanism that can support decision makers in prioritising sustainable maintenance of LVRRs.
As the main artery supporting economic activity in Japan, the Metropolitan Expressway comprises many structures that have been in use for a long time since their construction. Due to the challenging operating environment, which includes a higher proportion of heavy vehicles compared to other road bridges, deterioration is remarkably advanced. Maintenance costs have continued to rise, while structural safety must still be ensured within limited budgets and personnel. In recent years, research on deterioration prediction using machine learning and using big data accumulated from repeated inspections has been progressing to address these challenges. This paper aims to achieve risk-based maintenance and management that considers the uncertainty observed in actual deterioration phenomena and proposes a 'hierarchical deterioration prediction framework'.
The 2021 Mamuju-Majene earthquake (Mw 6.2) caused extensive structural damage in Mamuju. This study assessed the building damage distribution across the city of Mamuju using the damage index (DI) of the European Macroseismic Scale (EMS-98) and analysed its correlation with seismic amplification and ground deformation. The site response analysis utilised the Vs30 of multichannel analysis of surface wave surveys, while seismic amplification factors were calculated through one-dimensional site response analysis. In addition, interferometric synthetic aperture radar (InSAR) analysis was employed to assess ground deformation. The findings reveal that short-period spectral amplification (0.2 s) and construction quality were the dominant factors in structural failures, particularly affecting low-rise buildings (1-3 storeys). In contrast, long-period amplification (1.0 s) exhibited a weaker correlation with the DI, likely due to the limited number of mid- and high-rise structures in the study area. While evidence of liquefaction was observed, its overall impact on building damage was less significant. The integration of InSAR-derived ground deformation, geotechnical site response analysis, and field-based damage data proved to be highly effective for post-earthquake forensic assessment. This combined framework enabled the simultaneous evaluation of seismic amplification and permanent ground deformation, offering a more holistic understanding of damage mechanisms than conventional single-discipline approaches.
The literature on the structural performance of cold-formed steel (CFS) columns under fire conditions is limited. This study delivers critical insights into the buckling behaviour of back-to-back connected CFS columns with three different fire-resistant coatings. These columns underwent various heating and cooling procedures. Outcomes of this research offer significant implications for building codes and standards, particularly in understanding the effects of thermal exposure and cooling methods on CFS structural elements. In all specimens, the failure pattern observed was consistent, characterised primarily by significant buckling. Notably, sections coated with perlite demonstrated superior performance in fire resistance compared with other sections that were coated and heated. Among the coated specimens heated to 60 min and cooled using air, BC2AC (perlite) has the highest ultimate load at 268.93 kN, which is just 1.7% lower than the unheated section and 7.2% higher than BC1AC (gypsum) and 12.6% higher than BC3AC (vermiculite). This study contributes essential observations into the axial resistance of columns with various coatings under varying temperature and cooling conditions, potentially informing updates to standard guidelines regarding the impact of heating and cooling.
This paper investigates the causes of failure in the bottom outlet of the Dewana dam, Darbandikhan, Iraq, and proposes rehabilitation techniques to restore its strength. The outlet is 150 m long with a cross-sectional area of approximately 9 m2. Failures were reported in the slab and the walls of two 20 m segments adjacent to the core of the earth dam. Especially, the slabs in these segments suffered excessive deflection near the haunches, while excessive cracking and bar buckling were noticed in the upper part of the walls. It was found that there was a discrepancy in the gravity load calculations and that reliance on the arching effect required revision. The Halabjah earthquake may have also contributed to the failure. Constructing a new reinforced concrete outlet or filling the existing outlet with plum concrete are the proposed rehabilitation methods. The latter is preferred, as the former requires careful management. Finally, it was recommended to carry out the rehabilitation work urgently for the full length of the outlet, especially in sections where the backfill is more than 7 m.
This study proposes a high-resolution framework for defect detection and structural health monitoring in composite materials using a terahertz (THz)-based non-destructive testing (NDT) approach enhanced by deep image reconstruction. Experimental evaluations were conducted on Kevlar, glass fibre, and carbon fibre composites using THz time-domain spectroscopy. The framework integrates signal processing, machine learning, and statistical modelling to extract features such as absorption, reflection, and defect size. Principal component analysis, Random Forest feature ranking, and K-means clustering were used for defect classification and material characterisation. The proposed framework achieved a detection accuracy of 96.4%, outperforming conventional NDT techniques. Deep image reconstruction improved spatial resolution by 32%, enabling precise identification of micro-cracks, voids, and delamination. Weak-to-moderate correlations were observed between THz spectral features and defect propagation, demonstrating the effectiveness of THz imaging for real-time diagnostics. This research introduces a hybrid THz-based NDT framework combining advanced imaging and machine learning to improve defect quantification, classification, and prediction, supporting predictive maintenance and forensic failure analysis in aerospace, automotive, and civil infrastructure applications.
Rapid and quantitative assessment of bridge damage immediately after a disaster is critical for effective emergency response and early recovery. In this study, a method was developed to apply the YOLOv8 segmentation model to aerial imagery for detecting and classifying bridges based on the presence or absence of damage and to estimate bridge length and width from the detected regions to evaluate changes in shape dimensions. The results confirmed that for undamaged bridges, both bridge length and width could be estimated with high accuracy. In contrast, for damaged bridges, debris accumulation and inundation often resulted in only partial detection of the bridge, leading to a tendency for dimensional underestimation, particularly in the bridge length direction. Furthermore, it was confirmed that damage caused changes in the relative scaling relationship between bridge length and width, disrupting the original balance between these dimensions. These findings indicate that the observed dimensional shrinkage is not merely an estimation error but a quantitative characteristic of damage itself. This suggests that analysing changes in shape dimensions is a potentially effective method for detecting the presence of bridge damage.
Assembled H-shaped steel strut (AHSS) was applied in a deep excavation project. During the excavation process, abnormal flexural deformation of one AHSS was found. To evaluate the safety redundancies of the AHSS when its axial force reaches the designed level, a field axial loading test was conducted. It was found that, due to the weak region around the loading component, the horizontal and vertical displacements were larger for the monitoring points closer to the loading component. Steel columns showed a negligible contribution to the restriction of the horizontal displacement but relatively good vertical support of the AHSS. As compared with the normal deformation condition, the safety redundancies of the AHSS were all smaller when considering the abnormal deformation. However, the AHSS was still in a safe condition when its axial force slightly exceeded the designed value. To assess the contribution of different components of AHSS to flexural deformation and reveal the mechanism of abnormal flexural deformation, a simplified numerical simulation was conducted. The difference in horizontal movement of the wale at the two sides of each end of AHSS was found to be the leading factor causing the abnormal flexural deformation. Suggestions for avoiding such abnormal flexural deformation were given.
This study addresses the challenge of assessing bridge damage progression under compound disasters, specifically focusing on Bridge A in Japan affected by the 2024 Noto Peninsula Earthquake and subsequent heavy rainfall. We aimed to quantitatively evaluate potential changes in pier inclination using multi-temporal point cloud data (PCD) acquired by way of terrestrial LiDAR immediately after the earthquake and following the rainfall. Accurate pier isolation from complex PCD, complicated by environmental changes such as sediment deposition, was achieved using a deep learning semantic segmentation model enhanced with low-rank adaptation fine-tuning. Two distinct geometric analysis methods, M-estimator sample consensus (MSAC) cylinder fitting and principal component analysis (PCA) based on local surface normals, were independently applied to the segmented pier data to calculate inclination angles. Both MSAC and PCA consistently revealed a slight pier inclination ranging from 1.7° to 3° after the earthquake but detected no statistically significant progression after the heavy rainfall event. Our findings indicate no measurable progression of pier inclination during the observation period, demonstrating the effectiveness of the proposed workflow combining advanced scanning, segmentation, and robust multiple geometric analysis methods for monitoring structural stability and resilience in multi-hazard scenarios.
Around 38% of structural damage in metallic bridges occurs due to fatigue, with the aggravating factor being a structural phenomenon unknown until some decades ago, which leads to many existing structures being designed without addressing this process of structural degradation. Due to economic and environmental reasons, many metallic railway bridges see their operating time extended, leading to the accumulation of fatigue damage that can be significant and responsible for putting the safety of these structures at risk. Therefore, knowing the fatigue resistance properties of old metallic bridges’ structural materials becomes essential for applying advanced methodologies based on two-stage fatigue global–local approaches. One of these stages is characterised by the crack initiation phase, which is usually evaluated using local approaches based on stresses, strains, or energy. In most cases, only the monotonic tensile strength properties of old metallic bridge materials are known. For this reason, using estimation methods to obtain fatigue strength properties based on monotonic tensile strength properties becomes fundamental for such studies. This study explores various estimation methods for obtaining fatigue strength properties of metallic materials from old Portuguese bridges, revealing that the modified Mitchell method presents the best ranking when analysing all the materials studied.
The agreed-upon operational contingency cost is a key element of maintenance contracts, especially for the Natural Gas Reduction and Metering Stations. Disputes, claims, and failure to fulfil contractual obligations often stem from poor estimation of the risks associated with contingency costs. Currently, there are no standard rules or methods for companies to estimate these costs. As a result, some practices rely on rough estimates as a percentage of the total contract value without conducting any risk analysis. Others base their estimates on opinions from subject matter experts. This paper addresses the issue of improper contingency cost estimation by: 1) identifying the most influential factors affecting the value of maintenance contracts in the natural gas sector, and 2) developing a fuzzy logic risk analysis model to predict contingency costs based on the specific circumstances of contract signing. The effectiveness of this approach was validated through a real case study of an existing natural gas maintenance contract in Egypt. The results revealed that the most impactful factors were economic, contractual, operational, construction, and regulatory risks, while market and legal risks had the least impact. In addition, the findings show that the proposed model predicts the contingency cost with nearly 94.69% accuracy.
In the domain of building management, the absence of timely damage assessment and proactive preventive intervention plans, compounded by inherent aging processes, accelerates the degradation of structures. Current systems for condition assessment and decision support exhibit deficiencies in capturing and retrieving detailed information, resulting in ineffective decisions and significant financial losses. This comprehensive literature review explores condition assessment and maintenance of existing buildings through the integration of geographic information system (GIS) and building information modelling (BIM). GIS and BIM are essential tools for digitising data on the functional, spatial, and physical aspects of buildings. The review emphasises the synergistic potential of GIS and BIM, addressing challenges in interoperability, data modelling, and visualisation. Key findings underscore the importance of improved architectural frameworks to facilitate seamless data integration across diverse GIS, BIM, and facility management tools. The paper also highlights future research directions, including the integration of deep learning and machine learning for predictive maintenance of buildings integrated to their surroundings. In addition, it emphasises the role of Internet of Things technologies, automation, and advancements in 3D GIS and virtual reality for enhancing data collection, visualisation, and decision-making aspects in building condition assessment and maintenance.
Traditionally, reinforced concrete (RC) structures are designed in such a way that the input energy introduced during seismic events is diminished by the inelastic deformation of the structural elements because of which the building is accompanied by strength and stiffness degradation and results in the permanent deformations. Even though several active, semi-active and passive devices were implemented for the seismic response mitigation of the structures, every device has its own limitations. Therefore, a novel device, “Negative Stiffness Device (NSD)”, which works on self-centring mechanism, is presented to impart apparent weakening in the structure. The foremost concept of the current study is to evaluate the structural performance of the G + 6 soft-storey RC building by deployment of NSD at different positions of the structure by considering 24 different models. The Non-Linear Time History Analysis has been conducted when the structure is vulnerable to various seismic events. Based on the observed results, the seismic performance of the structure is found to be efficient with the implementation of NSD. The maximum acceleration and base shear of the building are diminished by (15%–30%) and (20%–42%), respectively, and the optimum models to be adopted for the best possible reduction of seismic parameters are studied.
While various algorithms are available to identify critical slip surfaces in soil slopes, practitioners often need to seed the surface search and validate identified surfaces. Identifying critical slip surfaces requires assessing competing cues related to driving and resisting moments, using heuristic reasoning and intuition. This study received 181 responses to a task asking participants to identify the most and least critical slip surfaces in three different slope stability scenarios. Most participants correctly identified one or two of the highest (mean = 1.6), and, on average, they identified one of the lowest factors of safety (mean = 1.0). Expertise measures only correlated with performance in the scenario with the highest factor of safety across the three scenarios and not the other scenarios. While this may suggest limited conceptual understanding, analysis of reasons provided by participants suggested that errors often stemmed from misapplied heuristics. For example, cohesion was sometimes overlooked, leading to incorrect identification of the lowest factor of safety; larger failing masses were sometimes incorrectly associated with lower safety in frictional materials; and masses were sometimes incorrectly assumed as driving moments rather than resisting moments when considering the point of rotation. Addressing these misconceptions could significantly enhance practitioner performance.
