Large Construction Projects Research Articles

Facile Preparation and Study of Self-Healing Water-Absorbing Expanding Elastomers.

The absorbent expansion elastomer plays a crucial role in engineering sealing and holds a promising future in this field as infrastructure continues to develop. Traditional materials have their limitations, especially when used in large construction projects where the integrity and reliability of the material are of utmost importance. In this work, a self-healing water-absorbing expansion elastomer was developed for continuous production at a large scale to monitor the sealing conditions of massive infrastructure projects. At room temperature, the material exhibited a repairing efficiency of 57.77 % within 2 h, which increased to 84.02 % after 12 h. This extended reaction time allowed for effective repairs when defects were detected. The material's strength can attain 3 MPa, placing it at the upper echelon among common self-healing materials, thereby granting it a certain level of durability in its application environment. The material's volume expansion rate reached 200-400 % to achieve effective sealing, and the functional filling of the filler endowed the material itself with a favorable external force induction effect and prevented heat accumulation. The conductive detection performance of the absorbent expansion elastomer was improved by utilizing triple self-healing strategies, including dipole-dipole interaction, ion cross-linked network, and externally-aided restoration materials. These strategies were combined with a double packing strategy to enhance the material's properties. This innovative elastomer can be applied in various fields such as tunnel construction, infrastructure development, aerospace sealing, and railway transportation, showcasing significant potential for diverse engineering applications.

Impact of leadership skills on the success of large and medium construction projects management in Yemen

Large and medium construction projects in Yemen often face budget and timeline overruns. While technical aspects are heavily focused on, the importance of leadership skills in project teams is often overlooked. This study aims to identify the impact of leadership skills on the success of these projects. A questionnaire was distributed to engineers working in both governmental and non-governmental institutions and organizations, with projects costing above $100,000. Out of the 385 questionnaires distributed, 340 responses were collected, and 246 valid responses were used for analysis. The findings indicate a strong correlation between leadership skills and project success, highlighting the significant role of effective leadership in guiding construction projects. Specific skills such as team management, problem-solving, prioritization, planning, decision making, conflict management, communication, and fostering team spirit are crucial in Yemen’s construction sector. Recommendations include the development of leadership skills programs, emphasis on collaborative team management, integration of experiential learning in education, continuous professional development, and research initiatives. These insights aim to enhance the resilience and success of construction projects in Yemen and provide valuable guidance to project managers, policymakers, and stakeholders in the sector.

A Study on Exploring and Prioritizing Critical Risks in Construction Project Assessment

According to Mc Kinsey Report (2015) large construction projects take 20% large time and 80% more budget to get finished. This highlights the importance of Risk Assessment to ensure work safety, cost management and quality construction.The study aims to identify and investigate critical risks in the evaluation of construction projects by utilizing the Principal Component Analysis (PCA) and the Weighted Average Index method. The risk factors for project assessment were identified in the following domains: Budget and Cost Management Risk, Schedule and Time Management Risk, Scope and Planning Risk, Safety and Regulatory Compliance Risk, Resource Management Risk, Communication and Stakeholder Management Risk, and Environmental and Sustainability Risk, through an extensive literature review and expert interviews. A survey was distributed to building operators in Hyderabad, India. The Weighted Average Index technique was used to evaluate 180 completed responses and identify risk indicators. To find underlying patterns, Principal Component Analysis (PCA) was used to gain a better understanding of the connections between these variables. Results identified reliance on essential resources, insufficient risk assessment, cash flow restrictions, and safety issues as top considerations, with currency exchange rate swings and delayed information dissemination ranking lower but still substantial. These insights provide essential advise to stakeholders in order to successfully minimize risks and improve project results. By using systematic risk assessment and management methodologies, building projects in Hyderabad and elsewhere may handle hurdles more effectively, assuring long-term sustainability and resilience.

Multi-Objective Resource-Constrained Scheduling in Large and Repetitive Construction Projects

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project’s size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects.

Identifying the interests of stakeholders in large construction projects: Based on Justification theory of Boltanski and Thévenot

In large construction projects, many stakeholders are involved, often with different interests. They look at the interests through the lenses of their conflicting worldviews, leading to the formation of different worlds that often have tensions among them. For example, to be viable, the projects must find a compromise between competition and collaboration. Hence, success in developing and implementing large construction projects requires analyzing the justifications of the world of stakeholders, identifying conflicts and creating agreements among them. Thus, the main purpose of this paper is to use Boltanski and Thévenot's theory to review the justifications of stakeholders’ various and understand the tensions and compromises in large construction projects. The research approach is exploratory qualitative, realized using a multiple case study. In this process, five cases were selected as five large construction projects from the four countries of Iran, Turkey, India and Ethiopia and were analyzed based on the data extracted from written, visual, and audio sources. The results indicate that while six stakeholder groups are identified in the sources, content analysis and clear evidence reveal that the industry, construction, and market groups dominate in large construction projects. In other words, the stakeholders often justify their benefits and losses through the lens of these three worlds. In addition, the research discusses tensions and possible compromises in large construction projects. The results of this research improve the insight and knowledge of project managers and contribute to the success of large construction projects. However, it faces limitations in terms of methodology and data adequacy.

Adjusted TextRank for keyword extraction in petrochemical project correspondence documents

A large petrochemical construction project is typically executed by multiple parties, all bound by contract agreement. During the execution phase, issues and problems may arise because the work details are not clearly specified in the contractual agreement. These issues are formally communicated and documented through written correspondence letters. By identifying important keywords within these formal letters, a comprehensive narrative of the project, including its associated issues, can be identified and analyzed. In this research, we introduce an adjusted TextRank algorithm that integrates external features from the Indonesian FastText language model and term frequency-inverse document frequency (TF-IDF) scores to identify important keywords within a dataset of correspondence letters of petrochemical projects. This enhancement involves refining phrase detection, semantic relationship estimation between words, and part-of-speech (POS) identification for words or phrases. Our results show that the proposed adjustments result in improved evaluation scores compared to the baseline standard TextRank and standard TF-IDF, respectively by 24.1% and 25% in terms of F-1 scores.

Automated construction scheduling using deep reinforcement learning with valid action sampling

The growing demand for buildings and infrastructures requires optimal construction schedules under real-world complexities. This paper presents an automated scheduling optimization method for large construction projects, considering real-world constraints and the need for rescheduling. A Deep Reinforcement Learning (DRL) model with a Valid Action Sampling (VAS) mechanism is proposed to optimize schedules. The method integrates a Graph Convolutional Network (GCN) for feature extraction and includes a reward shaping mechanism to expedite convergence. The proposed method outperforms traditional methods with reduced project duration and runtime in both scheduling and rescheduling cases. This advancement benefits construction managers seeking efficient and flexible project management. The findings inspire future research into broader and more practical construction scheduling solutions utilizing DRL.

Governance practices of large construction projects

Governance practices of large construction projects

Market research of russian medical construction facilities: cost estimation and investments in construction programs

Within the framework of the study, conditions are analyzed, cost estimates and other characteristics of large construction projects in the medical field are presented. The article analyzes investment projects of construction medical facilities in the Russian Federation. Plans for the development of some large medical networks are also briefly highlighted. A review of literature sources on health development programs of the subjects of the Russian Federation was conducted, federal target programs were considered, investment projects for the construction of medical facilities planned for completion in 2023-2025 were studied. Some analytical results of monitoring for 2020-2023 on the activity of the investment market in the direction of the Russian strategic and institutional design of the construction of medical facilities in the Russian Federation are presented.

Subcontractor Engagement in the Two-Stage Early Contractor Involvement Paradigm for Commercial Construction

Commercial construction projects (CCPs) in New Zealand contribute more to the economy than other project types. However, many face cost and time increases due to inadequate planning. Procurement pathways that involve contractors during design development provide more time to plan, collaboratively. Nevertheless, most projects are procured through traditional tender where contractors are only involved after detailed design. Through two-stage early contractor involvement (2S-ECI), contractors can provide design buildability advice for complex projects, contribute value management, carry out exploratory works, and order materials. The role of subcontractors in 2S-ECI can be significant. Six semi-structured interviews were conducted with clients, consultants, main contractors, and a subcontractor involved in large complex commercial construction projects. The findings build on the emerging body of knowledge about 2S-ECI by providing insight into subcontractor early involvement. Project complexity and market conditions were the main reasons for early subcontractor involvement. Common challenges include a lack of information sharing among the parties, non-competitive selection, and a lack of standard contract documentation. Opportunities for improvement include clarifying client expectations, educating stakeholders, and providing more equitable compensation for pre-construction services. Key drivers for subcontractor involvement include project complexity, market conditions, ordering long-lead-time systems, and performance specifications. Specialist early sub-trades include electrical, mechanical, structural steel, and façades. Subcontractors should typically be engaged as early as possible, often concurrently via main contractors to share performance risk. Pre-construction services provided by subcontractors include planning and sequencing; design buildability analysis; risk mitigation; value management; budget advice; systems procurement; design solutions; and document control systems. Advantages include obtaining specialist project knowledge and improving completion certainty. Producing a pre-construction services agreement (PCSA) for subcontractors may address challenges, as has been carried out for main contractors, but there is still a gap in the contractual framework for 2S-ECI for subcontractors.

Geophysical Survey as Part of Rescue Archaeological Excavation on Large Construction Projects—Case Study: Road I/16 Slaný–Velvary (Czech Republic)

Geophysical Survey as Part of Rescue Archaeological Excavation on Large Construction Projects—Case Study: Road I/16 Slaný–Velvary (Czech Republic)

An MCDM analysis of critical success criteria for medium and large construction projects in Australia and New Zealand

PurposeThe global construction industry has a history of poor project success, with evident and frequent overruns in cost and schedule. This industry is a highly interconnected and complex system in which the components, i.e. suppliers, contractors, end-users, and stakeholders, are delicately linked to each other, the community, and the environment. Therefore, defining and measuring project success can be challenging for sponsors, contractors, and the public. To address this issue, this study develops and analyzes a more comprehensive set of success criteria for medium and large construction projects.Design/methodology/approachAfter reviewing the existing literature, this study identified 19 success criteria for medium and large construction projects, which were categorized into five groups. The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) method was used to gain further insight into the interrelationships between these categories and explain the cause-and-effect relationships among them. Next, this study applied the modified logarithmic least squares method to determine the importance weight of these criteria using the fuzzy analytic hierarchy process.Findings28 project managers working in the construction industries in Australia and New Zealand participated in this study. Results suggest that “project efficiency” and “impacts on the project team” are cause criteria that affect “business success,” “impacts on stakeholders,” and “impacts on end-users.” Effective risk management emerged as the most crucial criterion in project efficiency, while customer satisfaction and return on investment are top criteria in “impacts on end-users” and “business success.”Originality/valueAlthough numerous studies have been conducted on project success criteria, multicriteria analyses of success criteria are rare. This paper presents a comprehensive set of success criteria tailored to medium and large construction projects. The aim is to analyze their interrelationships and prioritize them thoroughly, which will aid practitioners in focusing on the most important criteria for achieving higher success rates.

Risk Analysis and Simulation of Large Bridge Construction Based on System Dynamics

To reduce risk incidents in large bridge construction, it is necessary to study the interaction mechanisms and dynamic changes among various risk factors during the construction of large bridges. First, the evolution mechanism of construction risks for large bridges is analyzed, and a risk factor structure system is established. Then, based on system dynamics theory, a causal loop diagram and flow–stock diagram are constructed, and system dynamics equations for each variable in the flow diagram are established to build a system dynamics model. Finally, taking a large bridge construction project as an example, the variable equations are assigned actual values, and the construction risk level is simulated and analyzed. The results show that, ➀ continuous investment in safety funds can effectively reduce the overall risk level of the system, ➁ changes in the management risk subsystem have a significant impact on the overall risk level of large bridge construction, and ➂ increasing the safety investment ratio in both the personnel risk subsystem and the management risk subsystem can effectively reduce the total risk level of the system.

Morphology of polyhedral space habitat modules – Identifying the ideal form using multi-criteria analysis

Over the past fifty years, the form and function of space habitat modules have remained largely unchanged. While cylindrical modules are relatively simple to transport and deploy, their limited size poses a challenge to their efficiency and usability. In contrast, large space habitat construction projects are costly and present logistical difficulties. The development of polyhedral modules could bridge the gap between these two approaches, allowing for modular, polyhedral units to be assembled and linked together to create larger habitats more efficiently and with less risk. However, identifying the most suitable polyhedron for use in space remains a critical question. Previous research has explored the construction of polyhedral modules, but little rigor has been applied to identifying the most optimal form. This is a crucial issue because the first module to be used will likely set the standard for subsequent modules and any disadvantages present at that time will be perpetuated. Therefore, it is essential to identify the optimal form prior to constructing prototypes. This research paper employs multi-criteria decision analysis and sensitivity analysis to compare various candidate polyhedra across several evaluation metrics, including number of faces, volume to surface area ratio, and joint stress, among several other quantitative and qualitative metrics. The results demonstrate that the Rhombic Dodecahedron is a particularly suitable candidate compared to other forms analyzed. Thus, the Rhombic Dodecahedron should be considered the standard polyhedral form for future research involving the development of polyhedral modules.

From data to knowledge: Construction process analysis through continuous image capturing, object detection, and knowledge graph creation

Persistent issues of schedule deviations and cost overruns within large construction projects aggravate the construction industry’s global productivity concerns. However, how holistic, data-oriented methods can effectively be leveraged for investigating project performance and identifying potential bottlenecks during the construction phase remains unanswered. Our research addresses this issue with a novel approach encompassing data acquisition, object detection, geometric projection, and graph-based linking. Image data, continuously captured by crane-camera systems, gets transformed into higher-level information using an end-to-end deep learning-based pipeline that covers the detection of specific on-site objects and integrates it in a knowledge graph. The knowledge graph facilitates extracting precise construction metrics, identifying spatiotemporal irregularities, like work hotspots characterized by high activity and intensive work concentrations, but also phases with low activity. The proposed method improves learning from past construction data, aiding stakeholders and inspiring further research into real-time monitoring, predictive analytics, and data-integrated decision-making systems to reshape construction practices.

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.

Dispute Causes Analysis Using Analytical Hierarchy Process for Capstone Projects

Abstract: A disagreement, conflict, or subjective matter that leads to a legal process is termed as a dispute. The likelihood of a dispute arising in a loosely framed large scale construction project is high, which has an impact on project progress and can be considered a type of project risk. When different parties associated with the project are unable to resolve the disputes arising during the contract period it can be resolved by applying Alternate Dispute Resolution (ADR) technique which includes Arbitration, Litigation, Mediation and Conciliation. There are several types of construction disputes in construction projects like price escalation claim, change of work order claim, extra item claim, variation claim, loss of profit claim, delay claim which can be differentiate in various categories of dispute causes.

Roadway construction as a natural experiment to examine air pollution impacts on infant health

Traffic-related air pollution (TRAP) poses a significant public health risk that is associated with adverse birth outcomes. Large roadway infrastructure projects present a natural experiment to examine how resulting congestion change is associated with adverse birth outcomes for nearby populations. This study is designed to examine the influence of living close to a roadway before, during, and after a construction project using a difference-in-differences design. We integrated data on all large roadway construction projects (defined as widening of existing roads, building new roads, improving bridges, installing intelligent transportation systems, improving intersections, and installing or upgrading traffic signals) in Texas from 2007 to 2016 with Vital Statistic data for all births with residential addresses within 1 km of construction projects. Our outcomes included term low birth weight, term birth weight, preterm birth, and very preterm birth. Using a difference-in-differences design, we included births within 3 years of construction start and 2 years of construction end. In our main model, the exposed group is limited to pregnant individuals residing within 300 m of a construction project, and the control group includes those living within 300–1000 m from a project. We used regression models to estimate the influence of construction on infant health. We included 1,360 large roadway construction projects linked to 408,979 births. During construction, we found that the odds of term low birth weight increased by 19% (95% CI: 1.05, 1.36). However, we saw little evidence of an association for other birth outcomes. Contrary to our hypothesis of decreased TRAP after construction ends, we did not observe consistent improvements post-construction for pregnant individuals living within 300 m. Continued consideration of the influence of traffic congestion programs on birth outcomes is necessary to inform future policy decisions.

Amphibian Distribution and Habitats in Northwestern Saudi Arabia

More effort is needed to study wild animals, especially if they depend on a specific type of habitat. The amphibian community in Saudi Arabia needs additional research attention since not all potentially suitable habitats, particularly those in northern areas, have been studied. Different wetland habitats in Tabuk Province in northwestern Saudi Arabia were surveyed for amphibians in 2021 and 2022. Interestingly, only the Arabian toad was recorded at the sites visited. This finding contributes valuable data to the current amphibian distribution map of Saudi Arabia and suggests the great capacity of this species to inhabit habitats with different conditions and characteristics. Habitat destruction and potential sources of pollution were observed in some of the sites surveyed and, importantly, large construction projects are under way in the majority of amphibian habitats in Tabuk Province. Therefore, further effort is required to explore the less studied areas of Saudi Arabia to aid in the conservation of species, especially those such as amphibians that require specific habitat conditions.

Development of the conceptual business model of a third-party logistics provider for Indonesian small contractors

Third-party logistics (TPL) providers have shown positive impacts on supporting large construction projects. Nevertheless, their role in smaller construction still requires further clarification. Small contractors, facing logistics challenges in material supply, are frequently underrepresented in research. We propose a TPL model designed as an intermediary between suppliers and small contractors for consolidating materials. In the initial phase, TPL's business model should be developed for its business foundation. Therefore, this study aims to develop TPLS's conceptual business model that addresses Indonesian small contractors. Three interview themes were employed in this research, involving three distinct groups of small contractors to explore their capacities within construction work, their assessments of specific materials, and their logistical practices. Based on the nine blocks of the Business Model Canvas (BMC), this study found that TPL can ensure material supplies for small construction projects by coordinating sourcing, providing temporary storage, and delivering materials through building strategic partnerships with material suppliers, transportation service providers, and warehousing facility providers. Furthermore, TPL's conceptual business model allows small contractors to gain better access to material supply while providing suppliers with certainty of material purchasing demand. Nonetheless, achieving successful TPL implementation requires dedication from every stakeholder in their assigned roles.