Search Results (206)

This study aimed to investigate land-use and land-cover change and the associated change in the ecological stability of the model area Dobrá–Opatová (district of Trenčín, Slovakia), where increasing landscape transformation has raised concerns about declining ecological resilience. Despite the importance of sustainable land management, few studies in this region have addressed long-term landscape dynamics in relation to ecological stability. This research fills that gap by evaluating historical and recent LULC changes and their ecological consequences. Four time horizons were analysed: 1850, 1949, 2009, and 2024. Although the selected time periods are irregular, they reflect key milestones in the region’s land development, such as pre-industrial land use, post-war collectivisation, and recent land consolidation. These activities significantly altered the structure of the landscape. To assess future trends, we used the MOLUSCE plug-in in QGIS to simulate ecological stability for the future. The greatest structural landscape changes occurred between 1850 and 1949. Significant transformation in agricultural areas was observed between 1949 and 2009, when collectivisation reshaped small plots into large block structures and major water management projects were implemented. The 2009–2024 period was marked by land consolidation, mainly resulting in the construction of gravel roads. These structural changes have contributed to a continuous decrease in ecological stability, calculated using the coefficient of ecological stability derived from LULC categories. To explore future trends, we simulated ecological stability for the year 2030 and the simulation confirmed a continued decline in ecological stability, highlighting the need for sustainable land-use planning in the area. Full article

Rural road programs are essential for enhancing connectivity in remote areas, yet they frequently encounter schedule delays and budget overruns. This study explores the extent to which specific project characteristics influence these deviations in Colombian rural road contracts. A dataset comprising 229 projects was extracted from the national SECOP open-procurement platform and processed using the CRISP-DM protocol. Following the cleaning and coding of 14 project-level variables, statistical analyses were conducted using Spearman correlations, Kruskal–Wallis tests, and post-hoc Wilcoxon comparisons to identify significant bivariate relations I confirm I confirm I confirm hips. A Random Forest model was subsequently applied to determine the most influential multivariate predictors of cost and time deviations. In parallel, a directed content analysis of contract addenda reclassified 22 recorded deviation descriptors into ten internationally recognized categories of causality, enabling an integrated interpretation of both statistical and documentary evidence. The findings indicate that contract value, geographical region, and contractor configuration are significant determinants of cost and time performance. Additionally, project intensity and discrepancies between awarded and bid values emerged as key contributors to cost escalation. Scope changes and adverse weather conditions together accounted for 76% of all documented deviation triggers, underscoring the relevance of robust front-end planning and climate-risk considerations in rural infrastructure delivery. The findings provide information for stakeholders, policymakers, and professionals who aim to manage the risk of schedule and budget deviations in public infrastructure projects. Full article

Road infrastructure is the foundation of the predominant modes of transport, and its effective management is crucial to meet mobility needs. Although necessary for reconstruction, maintenance, and expansion projects, roadworks produce negative impacts, resulting in further risk for workers and drivers and failing to ensure sustainable development. The objective of this paper is twofold: Firstly, investigate the contributing factors to the occurrence of crashes in roadworks. Secondly, develop a model to estimate crash numbers in these areas. The results, which could support municipalities at the planning stage and implement policies for safe and sustainable development, are achieved by examining 121 sites, where 549 crashes occurred, and 25 contributing factors. The variables are divided into three categories: technical characteristics of the site, infrastructural, and environmental. Besides the conventional variables, a risk-increasing factor is calibrated. It assesses the impact of roadworks according to the manoeuvres imposed and the number of lanes. Consistent with previous findings, several variables related to the work zone layout, traffic conditions, infrastructure, and surrounding environment are correlated with the crash number. After performing a further statistical analysis, a multiple linear regression model, statistically significant (0.000) and suitable for accurately estimating the possible number of crashes (R²_adj = 0.41), is determined. Full article

Delays in road construction projects pose significant challenges in the Philippines, resulting in increased costs, project overruns, and unmet infrastructure goals. Common causes include poor financial management, inadequate subcontractor performance, deficient planning, and regulatory bottlenecks. This study aims to develop a comprehensive and data-driven framework to mitigate construction delays using the Analytical Hierarchy Process (AHP). The methodology integrates literature review, expert surveys, and pairwise comparisons to identify and prioritize critical delay factors. Experts from the Department of Public Works and Highways (DPWH), private contractors, and academia contributed to the AHP model. The results highlight seven major factor groups: client-related, contractor-related, consultant-related, materials, labor and equipment, contractual issues, and external influences. AHP analysis identified financial management, planning and scheduling, and regulatory coordination as the most impactful causes. Based on these findings, a strategic framework was developed and visualized using a Fishbone Diagram to present mitigation strategies tailored to each factor. While environmental engineering principles—such as material efficiency, energy use optimization, and impact assessments—are acknowledged, they serve as guiding themes rather than formal components of the framework. The study offers practical, stakeholder-validated recommendations for both pre- and post-construction phases, including real-time monitoring, risk anticipation, and improved multi-agency coordination. This framework provides a scalable tool for DPWH and related agencies to improve infrastructure delivery while supporting long-term sustainability goals. Full article

The construction industry, particularly in road projects, faces pressing challenges related to environmental sustainability and cost management. As road construction contributes significantly to environmental degradation and demands large-scale investments, there is an urgent need for innovative solutions that balance environmental impact with economic feasibility. Despite advancements in building technologies and energy-efficient materials, accurate and reliable predictions for environmental load and construction costs during the planning and design stages remain limited due to insufficient data systems and complex project variables. This study explores the application of machine-learning techniques to predict environmental loads and construction costs in road projects, using a dataset of 100 national road construction cases in the Republic of Korea. The research employs multiple regression analysis, regression tree models, and case-based reasoning (CBR) to estimate these critical parameters at both the planning and design stages. A novel aspect of this research lies in its comparative analysis of different machine-learning models to address the challenge of limited and non-ideal data environments, offering valuable insights for enhancing predictive accuracy despite data scarcity. The results reveal that while regression models perform better in the design stage, achieving error rates of 12% for environmental load estimation and 23% for construction costs, the case-based reasoning model outperforms others in the planning stage, with a 15.9% average error rate for environmental load and 19.9% for construction costs. These findings highlight the potential of machine-learning techniques to drive environmentally conscious and economically sound decision-making in construction, despite data limitations. However, the study also identifies the need for larger, more diverse datasets and better integration of qualitative data to improve model accuracy, offering a roadmap for future research in sustainable construction management. Full article

This publication aims to present the preliminary results of research on an innovative device designed to support the rehabilitation of drivers with neurological disorders, developed as part of a multidisciplinary project. The device was designed for individuals recovering from neurological diseases, injuries, and COVID-19-related complications, who experience difficulties with coordination and the speed of performing motor exercises. Its goal is to improve the quality of life for patients and increase their chances of safely driving vehicles, which also contributes to the safety of all road users. The device allows for controlled upper limb exercises using a diagnostic module, exercise program, and biofeedback system. The main component is a mechatronic driving simulator, enhanced with dedicated software to support the rehabilitation of individuals with neurological disorders and older adults. Through driving simulations and rehabilitation tasks, patients perform exercises that improve their health, facilitating a faster recovery. The innovation of the solution is confirmed by a submitted patent application, and preliminary research results indicate its effectiveness in rehabilitation and improving mobility for individuals with neurological disorders. Full article

The uncontrolled urbanization of Brazilian cities accentuates the imbalance between population demands and urban space planning. The integrated management of human and technological resources constitutes a fundamental governance strategy for the proposition of sustainable and effective responses to the challenges faced by cities. To generate references to public management, the objective of this research was to develop a management tool to verify the maturity level of Brazilian cities for smart city actions. A performance measurement system (PMS) organized smart city actions into 11 thematic areas, quantitatively measuring smart city actions in a down–top structure since the indicators and metrics are described in a standardized scale to obtain the general maturity index (GMI). The PMS was implemented in the city of Santa Maria/Brazil, where its GMI equal to 43.72% indicated a currently intermediate maturity level of smart city actions, mainly related to the low performance in the thematic areas of mobility, coexistence and reciprocity, and security and protection. To improve the current performance, five incremental actions were proposed, contemplating the key performance indicators “Public roads”, “Multipurpose lanes”, “Public accessibility”, “Accessibility signage”, and “Monitoring”, projecting a GMI equal to 49.75% and 55.78%, respectively, for an intermediate and an advanced maturity level scenario. Full article

To investigate the performance degradation of emulsified asphalt cold recycled mixtures (CRM) during service, this study selected a 10 km section of the cold recycled layer (CRL) from the Changjiu Expressway reconstruction project as the research subject. The deterioration patterns of key pavement performance indicators—including the Pavement Condition Index (PCI), Riding Quality Index (RQI), Rutting Depth Index (RDI), and Pavement Structure Strength Index (PSSI)—were analyzed in relation to cumulative equivalent axle loads over a 7-year service period. Concurrently, comparative evaluations were conducted on the mechanical properties, water stability, high-temperature performance, low-temperature crack resistance, and fatigue characteristics between in-service and laboratory-prepared emulsified asphalt CRM. The results demonstrate that after seven years of service, the emulsified asphalt cold recycled pavement maintained excellent performance levels, with PCI, RQI, RDI, and PSSI values of 92.6 (excellent), 90.1 (excellent), 88.5 (good), and 93.4 (excellent), respectively. Notably, while the indirect tensile strength and unconfined compressive strength of the CRL increased with prolonged service duration, other performance metrics—including the tensile strength ratio, shear strength, fracture work, and fracture energy—exhibited an initial improvement followed by gradual deterioration. Additionally, increased traffic loading during service led to a reduction in the residual fatigue life of the CRM. Interestingly, the study observed a temporary improvement in the fatigue performance of CRM during the service period. This phenomenon can be attributed to three key mechanisms: (1) continued cement hydration, (2) secondary hot compaction effects, and (3) diffusion and rejuvenation between fresh and aged asphalt binders. These processes collectively contributed to the partial recovery of aged asphalt strength, thereby improving both the mechanical properties and overall road performance of the CRM. The findings confirm that cold recycled pavements exhibit remarkable durability and maintain a high service level over extended periods. Full article

To implement the “Green Transition” in civil engineering, this study provides a new critical perspective analyzing the sustainability measures adopted by two European megaprojects. Government regulations and legislation, reward mechanism, technological innovations, the carbon evaluation system as well as tracking and monitoring systems are further discussed in this research to manage megaprojects in a more sustainable way. Document reviews, field trips (both exhibition area and construction sites), and in-depth interviews with relevant stakeholders were conducted regarding two European megaprojects, namely the A16 Ring Road in the Netherlands and Fehmarnbelt Tunnel in Denmark, when it comes to sustainability transitions. Notwithstanding the regional limitations of the selected case studies, the results illustrate that the implemented policies and regulations promote the sustainability transitions in projects and lead to environmental and societal benefits. Among the others, the requirement to quantify the carbon emissions is a central step during the tendering and execution phases of the megaprojects. Future studies need to comprehensively address the challenges related to project management and sustainable transitions as well as delve into other possible practices implemented locally in different locations. Local policies and regulations, innovation in technology and materials as well as the quantification of environmental impacts are key aspects to accelerate such change towards carbon neutrality. Full article

Road intersection projects are crucial for road infrastructure networks’ safe and efficient operation. Despite their importance, the construction of these projects faces significant challenges, such as delays, cost overruns, and litigation. Several causal factors for these phenomena have been identified as being associated with the construction schedule planning process. However, more studies need to be conducted to analyze the construction schedule planning process in detail. Therefore, the purpose of this paper is to analyze the schedule planning process of road intersection projects, emphasizing identifying and associating deficiencies in the planning process with their consequences on Colombian projects. The research method comprises three main stages: (1) identification of the schedule planning processes, (2) relation of the causes of deficiencies to the identified processes, and (3) analysis of the consequences of these deficiencies on the projects. A total of 29 schedule planning processes were identified, to which 22 causes of deficiency were assigned based on interviews with ten experts. The influence of these causes on schedule and budget deviations was analyzed based on the study of 25 Colombian road intersections. The results show that the lack of experience of the planner and poor estimation of material quantities strongly influence the generation of schedule and budget deviations in road intersection projects. Thus, this study contributes to improving the schedule planning process of road intersection projects by identifying the shortcomings and processes that can lead to schedule and budget deviations. Full article

This study presents a mixed-method systematic literature review (SLR) investigating the applications of Industry 4.0 (I4.0) technologies for enhancing sustainability in transportation infrastructure projects from a construction perspective. A corpus of 199 scholarly articles published between 2009 and November 2023 was meticulously selected from the Scopus database. The thematic analysis categorised the publications into four main clusters: infrastructure type, technology types, project lifecycle stages, and geographic context. The scientometric analysis revealed a burgeoning interest in the integrating of I4.0 technologies to enhance sustainability—particularly environmental sustainability. Among these, Building Information Modelling (BIM)-related tools emerged as the most extensively studied domain (33.50%), followed by the Internet of Things (IoT) and sensors (14%), and Artificial Intelligence (AI) (13.22%). The findings demonstrate that roads, highways, and bridges are the most studied infrastructure types, with BIM being predominantly utilised for energy assessment, sustainable design, and asset management. The main contributions of this review are threefold: (1) providing a comprehensive framework that categorises I4.0 applications and their sustainability impacts across transportation infrastructure types and project lifecycle stages, (2) identifying key technical challenges in integrating I4.0 technologies with sustainability assessment tools, and (3) revealing underexplored areas and providing clear directions for future research. The findings provide actionable insights for researchers and industry practitioners aiming to adopt integrated, sustainability-driven digital approaches in transport infrastructure delivery. Full article

This paper presents the development and analysis of a bridge bearing database consistent with the 2020 Italian Guidelines (LG2020), currently enforced by the Italian law for risk classification and management of existing bridges. The database was developed by putting together the contribution of 24 research teams from 18 Italian universities in the framework of a research project foreseen by the agreement between the High Council of Public Works (CSLP, part of the Italian Ministry of Transportation) and the research consortium ReLUIS (Network of Italian Earthquake and Structural Engineering University Laboratories). This research project aimed to apply LG2020 to a set of about 600 bridges distributed across the Italian country, in order to find possible issues and propose modifications and integrations. The database includes almost 12,000 bearing defect forms related to a portfolio of 255 existing bridges located across the entire country. This paper reports a preliminary analysis of the dataset to provide an overview of the bearings installed in a significant bridge portfolio, referring to major highways and state roads. After a brief state of the art about the main bearing types installed on the bridges, along with inspection procedures, the paper describes the database structure, showing preliminary analyses related to bearing types and defects. The results show the prevalence of elastomeric pads, representing more than 55% of the inspected bearings. The remaining bearings are pot, low-friction with steel–Teflon surfaces and older-type steel devices. Lastly, the study provides information about typical defects for each type of bearing, while also underscoring some issues related to the current version of the LG2020 bearing inspection form. Full article

Background: Governments face critical decisions regarding road remediation projects, requiring careful economic evaluation, especially in countries like Slovakia where road infrastructure is crucial for attracting foreign investment. These decisions are complex, involving short-term and long-term costs and revenues, along with inherent uncertainty about future outcomes. Traditional economic assessments often fail to capture the full scope of these factors, potentially leading to suboptimal choices. Methods: This study proposes four probability-based models: the Short-term Model (SM), Long-term-Short-term Model (LSM), Social Long-term-Short-term Model (SLSM), and Long-term-Short-term Model with a Time Aspect (TLSM). These models incorporate probabilistic functions to calculate expected costs and profits, considering various factors such as reparation costs, financial compensations, social costs, and time-related costs, as well as long-term benefits like increased investment and lives saved. Results: The proposed models were partially validated through an ex post analysis of a past road remediation project on road 1/18 (E50) under the Strecno castle cliff in Slovakia. The analysis demonstrated the models’ utility for multi-criteria decision-making in transportation problems, highlighting their ability to capture the complex interplay of economic and societal factors. Conclusions: The models enable governments to maximize societal benefit while mitigating potential risks, contributing to a more sustainable and efficient transportation sector. Future research could focus on refining the models and adapting them to other sectors beyond transportation. Full article

This study focuses on estimating transportation system-related emissions in CO₂ eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning algorithms. The case of Turkey was considered as an example. Model performance was evaluated using a dataset of Turkey, and future projections were made based on scenario analysis compatible with Turkey’s climate change mitigation strategies. This study also adopted a transportation type-based analysis, exploring the role of Turkey’s road, air, marine, and rail transportation systems. The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field. Full article

Bitumen is a significant component used in road construction. Traditionally, it is subjected to air-blowing processes at high temperatures (220–260 °C) to enhance its viscosity, rigidity, and oxidation characteristics. However, such approaches are often energy-consuming and result in extensive environmental issues, so more efficient and environmentally friendly techniques are needed. This review evaluates the emerging novel pathways for treating bitumen, with a particular focus on the role of ozone in the air-blowing process. By examining present studies, this review emphasizes the incorporation of ozone as an oxidizing agent to address the current challenges of long process times and high temperatures in the activation process and comprehensively demonstrates the enhancement of bitumen’s mechanical properties through ozone treatment. It also highlights the minimization of toxic emissions this achieves, especially highlighting the crucial role of ozone in improving the characteristics of bitumen in relation to the global trend toward making civil construction projects more environmentally friendly. The main aim of this review is to contribute to the development of new ideas in the field of bitumen modification and to encourage further advances in road construction from the standpoint of improving efficiency and minimizing environmental impacts. Full article