Infrastructure Construction Projects Research Articles

Analysis of the Consolidation Properties of Soil in the Mucherbari Regulator in Sunamganj

Analysis of the Consolidation Properties of Soil in the Mucherbari Regulator in Sunamganj

The Development of an Automated System for a Quality Evaluation of Engineering BIM Models: A Case Study

The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models’ data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.

Optimal Mix Design and Mechanical Properties of Rapid-Hardening Foam Concrete

This paper conducts compressive strength tests on foam concrete prepared under four factors and three levels through the design of orthogonal experiments. It delves into the phase change rules of the load–displacement curves obtained under various mix proportions. Furthermore, based on the 1-day and 3-day compressive strength values, the study explores different mix proportion results using range analysis and variance analysis methods, thereby determining the optimal mix proportion that can satisfy the maximum 1-day and 3-day compressive strength values. The results indicate that the compression process of rapid-hardening foam concrete includes four stages: initial compaction stage, elastic stage, yielding stage, and plateau stage, with each stage having different causes. Additionally, the sensitivity sequence of factors affecting the 1-day and 3-day compressive strength of rapid-hardening foam concrete is respectively rapid sulfoaluminate cement (α) > water-reducing agent content (δ) > foam content (β) > water-cement ratio (γ) and rapid sulfoaluminate cement (α) > water-cement ratio (γ) > foam content (β) > water-reducing agent content (δ). With 100% sulfoaluminate cement content, the 1-day and 3-day compressive strength values can reach 1.7054 and 2.5471 MPa, respectively, which are 13 times and 7 times the minimum values of 1-day and 3-day compressive strength under other admixtures. The analysis shows that the content of rapid sulfoaluminate cement has the most significant effect on the 1-day and 3-day compressive strength of rapid-hardening foam concrete, with foam content having the least impact on 1-day compressive strength and water-reducing agent content having the least impact on 3-day compressive strength. By integrating range analysis and variance analysis, the optimal mix proportion that simultaneously satisfies the maximum 1-day and 3-day compressive strength is determined to be 100% content of rapid-hardening sulfoaluminate cement, 4% foam content, 0.55% cement ratio, and 0.12% admixture content. Overall, this study provides theoretical support for the research and development of new rapid-hardening foam concrete materials and has significant practical implications for the emergency repair and construction of infrastructure projects.

Sustainability evaluation of urban large-scale infrastructure construction based on dynamic fuzzy cognitive map

Enhancing the sustainability of large-scale infrastructure construction is the foundation and trend of urban sustainable development and environmental protection. This paper proposes a perception and evaluation method based on Dynamic Fuzzy Cognitive Mapping (D-FCM) to address the dynamics, uncertainties, and implementation difficulties of sustainability evaluation in large-scale infrastructure construction. Using Hebbian rules to learn the experience and knowledge of experts, a dynamic evolution correlation matrix about evaluation indicators is obtained, and a D-FCM dynamic system model is established to obtain the sustainability evaluation results of large-scale infrastructure construction. The comprehensive sustainable evaluation value (K) of the selected subway project case is 0.934, which belongs to the V level in the sustainable level classification standard and has a good trend in sustainable development. Social public participation (S1), light pollution prevention (H1), wastewater discharge compliance rate (H2), soil pollution prevention (H3), and soil and water conservation and utilization (Z2) are the most significant and sensitive indicators for the sustainable development of large-scale infrastructure construction. Based on the interaction of influencing factors in the D-FCM model, an economically efficient control measures and decision-making plan can be proposed to improve the sustainability of large-scale infrastructure construction projects.

IDENTIFICATION, ASSESSMENT, AND PROPOSAL OF MECHANICAL RISK CONTROLS IN URBAN ROAD REPAVING INFRASTRUCTURE PROJECTS

This study aims to establish a comprehensive management of mechanical risks that may arise during the construction of infrastructure projects, specifically focusing on the process involved in urban road repaving. The case study selected is the "Repaving of General Rumiñahui Avenue between San Luis Avenue and Santa Clara Street, Quito, Ecuador". The objective is to mitigate various types of work-related accidents resulting from civil construction interventions. Mechanical risks are a significant concern in the construction industry, particularly in activities involving heavy machinery and specialized equipment. Like many other countries, Ecuador has construction-related labor regulations in place. Therefore, the main goal of this study is to identify, assess, and propose a system for controlling mechanical risks in the construction of repaving infrastructure projects to improve working conditions. The assessments of mechanical risks in urban repaving infrastructure projects will be analyzed using the "Colombian Technical Guide GTC 45" methodology. This quantitative and analytical approach applies current regulatory methods to define risk levels. The study will conclude by determining appropriate measures for labor control and the respective management of machinery and tools. These measures encompass preventive actions such as the use of personal protective equipment, appropriate signage, engineering controls, and worker training. Keywords: risk level, hazards, work accidents, sewerage, drinking water

Assessment criteria for inter-organizational collaboration in interconnected infrastructure projects

PurposeSocieties depend on interconnected infrastructures that are becoming more complex over the years. Multi-disciplinary knowledge and skills are essential to develop modern infrastructures, requiring close collaboration of various infrastructure owners. To effectively manage and improve inter-organizational collaboration (IOC) in infrastructure construction projects, collaboration status should be assessed continually. This study identifies the assessment criteria, forming the foundation of a tool for assessing the status of IOC in interconnected infrastructure projects.Design/methodology/approachA systematic literature study and in-depth semi-structured interviews with practitioners in interconnected infrastructure construction projects in the Netherlands are performed to identify the criteria for assessing the status of IOC in infrastructure construction projects, based on which an assessment tool is developed.FindingsThe identified assessment criteria through the literature and the practitioner’s perspectives results in the designing and development of a collaboration assessment tool. The assessment tool consists of 12 criteria and 36 sub-criteria from three different categories of collaborative capacity: individual, relational, and organizational.Originality/valueThe assessment tool enables practitioners to monitor the status of IOC between infrastructure owners and assists them in making informed decisions to enhance collaboration. The assessment tool provides the opportunity to assess and analyze the status of collaboration based on three categories (i.e., individual, relational, and organizational).

Development of workplace well-being indexes at construction sites

Purpose This study aims to develop workplace well-being indexes for construction sites of different project types (infrastructure, high-rise and low-rise). Accordingly, the study objectives are to identify the critical factors that affect workplace well-being at construction sites, compare the critical factors between different project types, categorize the critical factors into subgroups and compute indexes for the critical factors and subgroups. Design/methodology/approach Data from a systematic literature review and semi-structured interviews with construction industry professionals were used to extract 19 potential factors that affect workplace well-being. Then, a structured questionnaire survey was distributed, and 169 valid responses were collected. Finally, the data were analyzed using normalized mean analysis, agreement analysis, factor analysis and fuzzy synthetic evaluation. Findings The study findings revealed that there are 11, 11, 8 and 12 critical factors across overall infrastructure, high-rise and low-rise construction projects. Out of those, six critical factors are overlapping across project types, including “general safety and health monitoring,” “salary package,” “timeline of salary payment,” “working hours,” “communication between workers” and “planning of the project.” Accordingly, the critical factors can be categorized into two subgroups within each project type. Finally, the development of indexes shows that infrastructure construction projects have the greatest index compared to other project types. Originality/value This study contributes to filling the current knowledge gap by developing workplace well-being indexes at construction sites across different project types. The indexes would assist decision-makers in understanding the current state of workplace well-being. This increases the commitment and recognition of well-being across different construction project types.

Research on Over-the-Horizon Perception Distance Division of Optical Fiber Communication Based on Intelligent Roadways.

With the construction and application of more and more intelligent networking demonstration projects, a large number of advanced roadside digital infrastructures are deployed on both sides of the intelligent road. These devices sense the road situation in real time through algorithms and transmit it to edge computing units and cloud control platforms through high-speed optical fiber transmission networks. This article proposes a cloud edge terminal architecture system based on cloud edge cooperation, as well as a data exchange protocol for cloud control basic platforms. The over-the-horizon scene division and optical fiber network communication model are verified by deploying intelligent roadside devices on the intelligent highway. At the same time, this article uses the optical fiber network communication algorithm and ModelScope large model to model inference on real-time video data. The actual data results show that the StreamYOLO (Stream You Only Look Once) model can use the Streaming Perception method to detect and continuously track target vehicles in real-time videos. Finally, the method proposed in this article was experimentally validated in an actual smart highway digital infrastructure construction project. The experimental results demonstrate the high application value and promotion prospects of the fiber optic network in the division of over the horizon perception distance in intelligent roadways construction.

멕시코 국토 발전과 공간적 조정에 관한 시론적 연구

Mexico has been noted as a somewhat exceptional case in the 2000s, when the Pink Tide wave was exerting a powerful influence in South America. Thus after losing to Mexico’s two conservative parties, PAN and PRI, in 2006 and 2012, expectations for AMLO and his regime, which was elected as the candidate of the MORENA party in 2018, were higher than ever. In particular, it can be said that the regime succeeded in coming to power by emphasising the disconnection from the neoliberal policies implemented by the previous regimes, but in the economic growth policy, it still showed a basis for adjusting and maintaining the existing policy stance rather than an exclusive break with neoliberalism. Against this backdrop, this study aims to examine the reasons why the infrastructure construction project promoted by the AMLO regime has caused social criticism and backlash in the actual process, despite the fact that it specifies the goal of overcoming spatial and social imbalances. This study argues that the emphasis on procedural legitimacy to blunt the backlash against the infrastructure projects actively promoted by the AMLO regime can be understood in the context of the re-establishment or readjustment of the state-market-society relationship, rather than an exclusive break with the previous neoliberal regime. In other words, it is understood as part of a 'spatial fix' to deepen capitalist accumulation as advocated by critical spatial geographers. This study is a preliminary study that aims to overcome the limitations of domestic research on Mexico that has been mainly dealt with in the political, economic, and cultural contexts, and to contribute to the spatial and social discussions. Therefore, it is of academic significance in that it introduces the theoretical discussions of critical geographers who can understand capitalist accumulation and unbalanced land development and applies them to the case of Mexico.

Incentive modeling analysis in engineering applications and projects with stochastic duration time

Incentives are quite common to be utilized in engineering applications such as some infrastructure development projects or construction projects. Due to the increasing complexity of construction management and the continuing trend towards outsourcing of component or engineering outsourcing activities, we focus on the issue of incentive design. Time collaboration is one of the main focuses of random project duration time in parallel projects. In this article, we consider a setting where a manufacturer outsources two parallel subtasks to two different suppliers, and the manufacturer is time sensitive. On the premise that the project completion time follows the exponential distribution, some models are established to compare the proposed incentives and we get the comparative analysis of the proposed incentives. This paper puts forward three kinds of time-based incentive mechanisms, namely, deadline incentive mechanism, competition mechanism and mixed incentive mechanism. We do modeling analysis for all incentive mechanisms. We get the optimal work rates determined by suppliers and compare various incentive mechanisms to maximize manufacturers' profits.

Project management and partner banking as tools for the recovery concept in Ukraine

The main purpose of this study is to investigate the effect of project management and partnership banking. Due to the shortage of investment, Ukraine always needs to plan for finance resource. All over the world the partnership banking is becoming an increasingly common and attractive type of investment in projects. In the conditions of modern Ukraine, the need to restore industrial and infrastructure construction projects one can predict a significant growth in the construction services market and the need of investment. In this study, the ways of possible increasing of investment in construction industry have been discussed based on the use of a logical-structural approach in project management and partnership banking as a source of investment.

Search on Risk Assessment of PPP Project Financing Based on ISM-FAHP Model

With the rapid development of China's economy, the demand of large-scale infrastructure construction projects is increasing day by day, but the shortage of funds has become the bottleneck restricting the progress of projects. In order to solve this problem, the Chinese government has gradually implemented the PPP project financing model in the field of infrastructure to attract social capital to participate and share the risk. However, with the implementation of this model, the problem of financing risk has gradually become prominent. Therefore, this study aims to provide scientific theoretical support and practical suggestions for relevant decision-making through in-depth analysis of PPP project financing risk and application of ISM-FAHP model.

Enhancing of uniaxial compressive strength of travertine rock prediction through machine learning and multivariate analysis

Indirect methods for predicting material properties in rock engineering are vital for assessing elastic mechanical properties. Accurately predicting material properties holds significant importance in rock and geotechnical engineering, as it strongly influences decisions about the design and construction of infrastructure projects. Uniaxial compressive strength (UCS) is one of the most important elastic mechanical properties for understanding how rocks and geological formations respond to stress and deformation. However, the standard UCS test faces several challenges, including its destructive nature, high costs, time-consuming procedures, and the requirement for high-quality samples. Therefore, there is a growing demand for indirect methods to estimate UCS, which are invaluable tools for evaluating the elastic mechanical properties of materials. The study aimed to comprehensively analyze the relationships between UCS of travertine rock samples collected from the Dead Sea and Jordan Valley formations and seven different rock indices by utilizing parametric and non-parametric methods. The laboratory results indicate that the study area's travertine rock possesses high-quality and desirable properties. The results reveal that certain rock indices, such as Schmidt hammer, Leeb rebound hardness, and Point Load, strongly correlate with Uniaxial Compressive Strength (UCS). Conversely, other indices, specifically dry density, absorption, pulse velocity, and porosity, exhibit a considerably weaker or very weak relationship with UCS. The paper employs three machine learning techniques, namely the Tree model, k-nearest neighbors (KNN), and Artificial Neural Networks (ANN), to develop predictive models for rock strength. The models were trained on a dataset of rock properties and corresponding mechanical strength values. The study's results revealed that the M5 tree model is the most suitable method for predicting UCS. It demonstrates robust performance across a spectrum of metrics and boasts low prediction errors. Following the M5 tree model are the KNN, ANN, and regression methods in descending order of performance.

Non-destructive evaluation (NDE) methods used for condition assessment of concrete structures

As an integral part of the construction industry, concrete is an important part of the building materials industry, as well as an important material for infrastructure and engineering construction projects. It is widely used in various industries. In recent years, with the improvement of the quality awareness of the whole people, the engineering quality inspection has also received more attention. The previous concrete quality inspection methods have different degrees of damage to the original structure. This paper focuses on the state of concrete structure and its detection method, and introduces the EDN technology. At the same time, through comparison, practical experiment, theoretical research and other research methods to describe EDN technology. Based on the research results, it is concluded that ECN technology can provide a more comprehensive concrete quality report, which is beneficial to the maintenance, repair and restoration of Bridges and old buildings, and help engineers use.

Automated system for high-accuracy quantity takeoff using BIM

Reliable construction project cost estimation relies on accurate quantity takeoffs (QTOs), traditionally done with error-prone 2-D drawings, but the emergence of Building Information Modeling (BIM) has improved QTO speed, accuracy, and reliability. However, the quality of BIM models can influence quantity accuracy, an aspect often overlooked in research on automating QTO with BIM. This paper develops an integrated framework to automatically extract and visualize construction quantities from BIM models. The system includes a Quantity Precision Check (QPC) module for automating more accurate QTO outputs. These outputs are then automatically transferred to an external database and visualized through a user-friendly dashboard on the Microsoft Power BI platform. This dashboard allows for comparisons between 2-D and BIM quantities and helps track changes in quantities over time due to design changes. The system was successfully validated in an infrastructure construction project in Canada, where it identified a 39% inconsistency in wall elements' material quantities and reduced steel quantity by 10% due to design changes. Overall, the system offers a speedy, reliable, and efficient tool for construction estimators, site managers, and cost control teams to provide accurate quantities to the procurement team and make swift decisions in response to design change impacts on construction costs.

Perspectives of 3D Probabilistic Subsoil Modeling for BIM

Building information modeling (BIM) in the planning and construction of infrastructure projects, such as roads, tunnels, and excavations, requires the generation of comprehensive 3D subsoil models that encompass relevant geological and geotechnical information. Presently, this process relies on the deterministic interpolation of discrete data points obtained from exploratory boreholes and soundings, resulting in a single deterministic prediction. Commonly employed interpolation methods for this purpose include radial basis function and kriging. This contribution introduces probabilistic methods for quantifying prediction uncertainty. The proposed modeling approach is illustrated using simple examples, demonstrating how to use sequential Gaussian and Indicator Simulation techniques to model sedimentary processes such as erosion and lenticular bedding. Subsequently, a site in Munich serves as a case study. The widely used industry foundation classes (IFC) schema allows the integration of the model into the BIM environment. A mapping procedure allows transferring voxel models to the IFC schema. This article discusses the significance of incorporating uncertainty quantification into subsoil modeling and shows its integration into the BIM framework. The proposed approach and its efficient integration with evolving BIM standards and methodologies provides valuable insights for the planning and construction of infrastructure projects.

Comparison of predicted and measured noise levels from major infrastructure construction projects

Urbanization has led to major infrastructure projects increasingly being constructed within heavily populated residential areas. Prediction and management of noise impacts associated with construction works is an increasingly important consideration during both the planning and delivery phase of projects. However, accurate prediction of construction noise is often challenging, due to the dynamic nature of infrastructure construction sites, with a large number of mobile and intermittent noise sources. Particularly during the planning phase, there is typically a lack of detailed information required to support accurate noise modeling. Noise predictions are nevertheless relied upon to inform the project team and surrounding community of the expected noise impact of upcoming works and to determine the most appropriate mitigation and management measures to adopt. In this paper, we compare noise levels predicted using a variety of common methods, with attended and unattended noise monitoring results obtained on a number of recent projects.

Der Weg zum Bauen 4.0 in der Produktion und Bauausführung von Infrastrukturprojekten

AbstractDie Baubranche stieht sich mit tiefgreifenden Veränderungen konfrontiert. Neben der Umsetzung der wohl wichtigsten Anforderung – einer nachhaltigeren Baukultur – müssen u. a. der zunehmende Fachkräftemangel und der steigende Bedarf an Infrastrukturbaumaßnahmen bewältigt werden. Die Lösung dieser Herausforderungen wird der Branche mit den bisher über Jahrzehnte praktizierten Verfahren und Prozessen nicht gelingen. Umso wichtiger ist es, den Weg für die Einführung von Bauen 4.0 zu ebnen. Im vorliegenden Beitrag wird erläutert, was sich hinter dem Begriff Bauen 4.0 verbirgt und welche Rahmenbedingungen die Umsetzung aktuell erschweren. Darauf aufbauend werden mit dem Schwerpunkt der für ausführende Bauunternehmen wichtigen Produktions‐ und Bauphase von Infrastrukturprojekten Empfehlungen, Maßnahmen und erste Ansätze zur Umsetzung präsentiert.

Construction Digital Technology Assimilation and Absorption Capability Using Measurement Invariance of Composite Modeling

Digital technology is evolving, and construction companies now have various choices of these technologies to implement into daily business. This has, however, resulted in a growing argument on how digital technologies can be accepted in various contexts. However, most arguments are based on technology acceptance models with the core concept of individual behaviors, which ignores the influence of other stakeholders and organizational characteristics. This paper aims to present a novel model representing the successful practice of the assimilation process for digital construction technologies that should be considered before any technology acceptance analysis. The model was tested via empirical investigation with 372 practitioners from Australia involved in building and infrastructure construction projects. Structural equation modeling, measurement invariance of composite models, and some semistructured interviews were used to test and verify the relationships between key variables of the model. The results suggest that the capability of assimilation can be enhanced by improving innovativeness and the relevant operational processes, with a t-value of >6.27. The findings show that a project’s likelihood of successful technology assimilation can also be increased if vendors are supportive and the company improves its operating performance assessment and absorption capacity. The findings clarify theoretical and practical contributions for a better understanding of critical factors of assimilation and provide a tool to potential company users for predicting the success of technology assimilation.

Soil Stabilization by Using Lime and Fly Ash at Ahirwadi

Abstract: Soil stabilization is a crucial aspect of civil engineering, particularly in the construction of infrastructure projects. This study investigates the effectiveness of lime and fly ash as soil stabilizers and their combined application for enhancing soil properties. The objective of this research is to evaluate the changes in the geotechnical properties of soil treated with lime and fly ash, including compressive strength, shear strength, and durability. In this experimental study, different proportions of lime and fly ash were mixed with the soil samples to determine the optimal combination that yields the highest improvement in soil characteristics. The laboratory tests conducted on the stabilized soil samples included compaction tests and unconfined compressive strength tests. The samples were cured for different periods to assess the long-term performance and durability of the stabilization technique. The results indicate that the addition of lime and fly ash significantly improves the soil's engineering properties. The lime effectively increases the soil's pH, reducing plasticity and improving workability. The fly ash, on the other hand, enhances the pozzolanic reactivity and improves the soil's strength and durability. The combination of lime and fly ash demonstrates synergistic effects, leading to further improvements in the stabilized soil's properties.