Construction Project Management Research Articles

Unveiling the Driving Factors of Emerging Trends in the Ghanaian Construction Industry: An Evaluative Analysis of Push and Pull Factors

There is a significant change in the Ghanaian construction sector, driven by the convergence of emerging trends. The study investigated the underlying factors influencing these emerging trends in the Ghanaian construction industry, focusing on both pull and push factors. A mixed-methods approach was employed, combining survey data with qualitative analysis, to gain a comprehensive understanding of the drivers shaping the industry's development. The study was conducted within three cities (Cape Coast, Kasoa, and Winneba) in the Central Region of Ghana. Fifteen (15) construction companies were purposively selected based on their interest in technological advancements within the construction ecosystem. Five (5) categories of construction professionals, project managers, quantity surveyors, architects, structural engineers, safety officers, and site supervisors employed by the aforementioned fifteen (15) construction companies were considered for the study. Data collected were analyzed utilizing SPSS software version 20. Push factors highlighted environmental degradation, technological innovation, and infrastructure renewal as significant motivators. Pull factors emphasized the industry's extrinsic motivations, including community development, cultural considerations, and economic growth. The analysis revealed that both push and pull factors play crucial roles in driving the trends within the industry. Qualitative interviews conducted as part of the study provided deeper insights, identifying six key themes: regulatory compliance and market competition, technological advancements, skill development, economic considerations, leadership and strategic vision, and customer demand. These themes underscore the complex relationship between external pressures and internal motivations. Correlation and regression analyses indicated significant relationships between trend recognition and both push and pull factors, emphasizing the interplay between external pressures and aspirational drivers in shaping industry trends. This study contributes to a deeper understanding of the dynamic forces driving change in the Ghanaian construction sector, offering substantial insights for stakeholders. These insights can help stakeholders navigate challenges and capitalize on opportunities for sustainable growth and development. The findings underscore the importance of balancing technological advancements with socio-economic goals to foster a robust and forward-thinking construction industry in Ghana.

Hybrid Data Augmentation for Enhanced Crack Detection in Building Construction

Quality management in construction projects necessitates early defect detection, traditionally conducted manually by supervisors, resulting in inefficiencies and human errors. Addressing this challenge, research has delved into automating defect detection using computer vision technology, yet progress has been impeded by data limitations. Numerous studies have explored generating virtual images to tackle this issue. However, these endeavors have fallen short in providing image data adaptable to detecting defects amidst evolving on-site construction conditions. This study aims to surmount this obstacle by constructing a hybrid dataset that amalgamates virtual image data with real-world data, thereby enhancing the accuracy of deep learning models. Virtual images and mask images for the model are concurrently generated through a 3D virtual environment and automatic rendering algorithm. Virtual image data are built by employing a developed annotation system to automatically annotate through mask images. This method improved efficiency by automating the process from virtual image creation to annotation. Furthermore, this research has employed a hierarchical classification system in generating virtual image datasets to reflect the different types of defects that can occur. Experimental findings demonstrate that the hybrid datasets enhanced the F1-Score by 4.4%, from 0.4154 to 0.4329, compared to virtual images alone, and by 10%, from 0.4499 to 0.4990, compared to sole reliance on real image augmentation, underscoring its superiority. This investigation contributes to unmanned, automated quality inspection aligning with smart construction management, potentially bolstering productivity in the construction industry.

Duration and resource constraint prediction models for construction projects using regression machine learning method

PurposeThe construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties.Design/methodology/approachThe dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor.FindingsThe prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R2 was from 0.95 to 0.97, indicating better efficiency compared with other prediction models.Research limitations/implicationsThe researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices.Practical implicationsThe research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes.Social implicationsDelays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements.Originality/valueThis is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.

Analyzing the Impact of Economic Performance on the Interplay between Financial Management Strategies and Profitability in the Iraqi Context

Purpose - This study examines how economic performance affects financial management approaches and profitability in Iraq's construction business. This research examines how financial management strategies affect profitability and economic success. Design/Methodology/Approach—Construction project and site managers in Iraq were surveyed online for the study. Given the scope and magnitude of the research, this study's 85.3% response rate is impressive. In-depth statistical analysis was performed using Partial Least Squares Structural Equation Modelling (PLS-SEM), which examined economic performance, financial management, and profitability. Findings - The instance indicates that the lack of organization-wide integrated systems, common across enterprises, contributes to this problem. The major issue is that the organization's information systems strategy never expressly prioritizes KM-enabling schemes. Practical Implications—The study's conclusions have major practical ramifications. Iraqi enterprises should prioritize resource optimization, cost control, and strategic financial planning to improve economic performance. In addition, governments should promote building projects through incentives, subsidies, and tax policies that boost economic productivity and profitability. These policies are available here. Originality/Value—This study adds to the literature by stressing economic performance as a mediator between financial management methods and profitability in Iraq's construction sector. It examines the dynamics of this link in Iraq's building sector. With the new data, firms may improve financial performance by making informed judgments and using efficient financial management procedures.

Making logistics a central core in complex construction projects: a power-dependency analysis

In complex projects, such as hospital constructions, there are large interdependencies between the actors involved, especially relating to the flow of materials and resources. As new hospital buildings are often built on existing blocks and campuses, ongoing hospital operations may hinder logistics and construction operations and vice versa. To handle these issues, it has become increasingly common to implement construction logistics setups (CLSs). The purpose of the paper is to analyse how the centrality of the CLS in the project organization affects the coordination and conflict level in complex construction project management using a power dependency lens. The study is based on case studies of six hospital projects in the Nordic countries. Based on a cross-case analysis it is identified that: 1) from a logistics management perspective we identify three different foci of the CLS; i) security for hospital patients and staff, ii) on-site material flow coordination, and iii) flow coordination to and from site, and 2) from a project management perspective the main findings point towards the importance of centrality of the CLS in the project management organisation in order to ensure enough power to mandate enforcement of coordination.

Innovative Model for Assessing the Level of Failure Risk in Construction and Buildings

This research explores risk management in construction projects to prevent potential failures due to natural factors or human errors. The objective is to develop a risk assessment model for failures in construction and building projects, focusing on identification, categorization, risk level determination, and model development. The research benefits encompass theoretical contributions to construction risk management, governmental policy guidelines, operational assistance in project risk management, and enhancements in the safety and quality of construction projects. This research innovates by expanding research variables, incorporating failure risk level assessments, and utilizing technological approaches. The research methodology combines quantitative and descriptive approaches, focusing on failure risk factors from service/contractor providers, consultants, and project owners. Data is obtained through literature studies, secondary data, and primary data via questionnaires. The analysis involves factor analysis, importance index, impact probability matrix, and the Partial Least Square (PLS) method for structural analysis. The research conclusion identifies failure risk factors including financial aspects, management, equipment availability, and natural impacts such as floods and earthquakes. The risk assessment model categorizes risks into high, medium, and low, enabling appropriate anticipatory measures. This model provides guidance for stakeholders to mitigate risks, improve quality, and maintain the integrity of construction projects and buildings, supporting safety and success throughout project stages. This research makes a significant contribution to construction project risk management and construction quality improvement.

Enhancing Sustainable Safety Practices in Construction: Insights from Cultural and Socio-Economic Analysis

Exploring cultural diversity and socio-economic factors offers a holistic approach to fostering sustainable safety practices within the construction industry. This study investigates the relationship between cultural and socio-economic factors and their influence on safety practices within the construction industry, focusing specifically on Saudi Arabian construction. Employing a comparative research design, data were gathered through a questionnaire distributed among various stakeholders, including construction workers, safety officers, project managers, supervisors, and other professionals involved in construction projects. Structural equation modeling (SEM) was utilized to analyze the relationships between cultural, socio-economic, and safety practices. The findings showed significant associations of cultural and socio-economic factors with safety practices and attitudes. Specifically, positive workplace norms and values, effective communication styles, higher income levels, and better education and literacy levels were found to positively influence adherence to safety guidelines and the effectiveness of training programs. Conversely, job insecurity exhibited a negative impact on safety-related outcomes and training effectiveness. This study demonstrates the importance of fostering a positive safety culture within organizations, emphasizing leadership commitment, effective communication, and employee engagement. It also highlights the significance of addressing socio-economic factors, such as job security, income levels, and education, to promote a safer work environment and enhance safety practices. Practical implications derived from the research findings offer actionable insights for organizations to develop targeted interventions aimed at improving sustainable safety practices and training effectiveness within the construction industry in Saudi Arabia.

The Construction project management through a holistic view of sustainability

Contextualization: the construction sector is innovating every day in the postulation andcreation of new environmentally responsible projects, to mitigate the negative alterationsproduced by the processes carried out in the elaboration and erection of structures. Knowledge gap: for this reason, the field of this research aims to describe the triad ofsustainability in the management of construction projects, leading to a successful outcomeof a sustainable industrial project. Purpose: the fundamental purpose of the work is to incorporate the economic,environmental, and social approaches to the known practices of civil works projectmanagement. Methodology: the methodology used in this manuscript was qualitative and descriptivecorrelational documentary analysis was applied to identify and analyze the most importantindicators in the management of construction projects with a focus on sustainability. Theresearch allowed distinguishing a considerable number of key indicators that are used in thecurrent jargon and that could be implemented in new projects with the purpose of seekingholistic synergies that mitigate the uncertainty of the proposed goals. Results and conclusions: as a result of this study, measurement factors were identifiedand categorized in order to facilitate the conceptualization and consideration of sustainabilitypractices for project managers. This study contributes to the mapping of opportunities forimprovement in the sustainability of construction projects based on the management ofindicators and highlights the relevance of sustainable management for the constructionindustry.

USING NEURAL NETWORK TECHNOLOGY IN CONSTRUCTION ACTIVITIES

The article discusses the basics of using neural networks in the activities of construction organizations. The main goal of the study was to substantiate the advantages and prospects of using neural networks in the construction industry. As part of the study, the essence of this variant of machine learning was initially determined, their classical architecture was justified, key elements were identified, and the principle of operation was described. Subsequently, options for using neural networks within the construction industry were systematized (design of construction projects, analysis of costs for the implementation of construction projects, management of construction projects, monitoring of labor and equipment productivity, assessment of the quality of materials used and inventory management, ensuring energy efficiency, risk analysis and ensuring the safety of work performed, etc.) and a distinction was made between the areas of application of neural networks within the stages of the life cycle of construction projects, which formed the basis of scientific novelty. The results of the study can be applied in the activities of construction organizations to increase the efficiency of work carried out at various stages of construction.

The impact of environmental stimuli on the psychological and behavioral compliance of international construction employees.

This study explores the overlooked psychological and behavioral dynamics of employees in compliance management, applying the Stimulus-Organism-Response (SOR) framework to assess environmental stimuli's impact on employees in international construction projects. A scenario-based survey involving 270 international construction employees was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA), focusing on the relationship between environmental stimuli and compliance intentions. Findings categorize environmental influences on compliance into internal and external organizational dimensions, highlighting the significant impact of internal factors on compliance intentions. Key determinants identified for high compliance intention include individual traits and organizational climate, while project pressures, rules and regulations, and cultural differences show variable influence. This study enhances the understanding of the psychological factors driving non-compliant behaviors and introduces a binary micro-ecological approach to compliance management, effectively integrating individual and project organizational elements. In contrast to traditional corporate governance approaches, this strategy emphasizes the role of project organizational micro-ecology in the management of international construction projects. The strategy aims to improve compliance management among international contractors by influencing the psychological and behavioral compliance of frontline employees.

Enhancing Index and Strength Properties of Black Cotton Soil Using Eco Friendly Material: GGBS

This research investigates the efficiency of utilizing Ground Granulated Blast Furnace Slag (GGBS) for soil stabilization, aiming to improve index and strength properties and address environmental sustainability through waste utilization. Various laboratory tests, including compaction, Atterberg limits, and unconfined compressive strength evaluations, were conducted on soil samples of Hingoli district amended with GGBS at different ratios. The results reveal significant enhancements in engineering properties, indicating the potential of GGBS in soil stabilization practices. The findings underscore the economic and environmental benefits of incorporating GGBS into soil stabilization techniques, offering a sustainable solution for both soil improvement and waste management in construction projects. This study contributes to the advancement of sustainable engineering practices by demonstrating the effectiveness of waste utilization in enhancing soil performance and mitigating environmental impacts. Keywords-: GGBS, Atterberg Limit, Hingoli, Unconfined Compressive Strengtht, Speicfic Gravity

Managing construction project risks in turbulent times: a stakeholders perspective

PurposeThe construction industry is unique but with uncertainties. This is because of the operating environment. This intricacy gives rise to several construction risks and is compounded in developing countries’ turbulent times. If not managed, these risks enhanced in turbulent times could negatively impact the Nigerian construction projects’ cost, time, quality, and performance. Hence, this study investigated the perceived encumbrances facing construction risk management techniques and identified measures to promote sustainable-based construction risk management in turbulent times.Design/methodology/approachThe researchers adopted a qualitative approach and achieved saturation with 28 participants. The participants were government policymakers, quantity surveyors in government ministries/agencies/departments, consultant engineers, consultant architects, consultant and contracting quantity surveyors, and construction contractors knowledgeable about construction risk management. The research employed a thematic analysis for the study’s data.FindingsFindings identified turbulent times related to the industry and major techniques for managing construction project risks in the Nigerian construction industry. It revealed lax adoption and implementation of practices. Also, the study identified major encumbrances facing construction risk and proffered initiatives that would promote sustainable-based construction risk management in turbulent times.Originality/valueThis study investigates encumbrances and suggests measures to promote construction project risk management in turbulent times in Nigeria. Also, the study contributes to the literature’s paucity, uncovering perceived encumbrances and evolving organisations’ management styles to imbed sustainable-based risk management practices by qualitative research design method.

Analyzing the Application of BIM Technology in Construction Project Management

Against the backdrop of the booming economic development in China, the construction industry has also witnessed rapid growth. The overall construction process of building projects is no longer a simple “drawing-construction” model, but has become more complex. This study aims to fully utilize BIM technology and apply it throughout the entire construction management process, with the goal of enhancing the quality of construction management, streamlining operational workflows, and improving efficiency. This paper analyzes the current research status both domestically and internationally, and identifies several implementable strategies to enhance the application efficiency of BIM technology in construction project management, considering its use in the planning, design, and construction stages. The research findings presented will greatly assist project management in the field of construction.

Application of Soft Computing to Address Uncertainty in Construction Project Management: A Systematic Literature Review

Application of Soft Computing to Address Uncertainty in Construction Project Management: A Systematic Literature Review

Information Support for Decision-Making in the Management of Regional Investment and Construction Projects: A Systematic Approach

Information Support for Decision-Making in the Management of Regional Investment and Construction Projects: A Systematic Approach

INTEGRATION OF APPLIED IPD MODULES INTO THE COMPOSITION OF ORGANISATIONAL AND TECHNOLOGICAL TOOLS FOR CONSTRUCTION

Modern construction projects are characterised by complex multifunctional relationships that require processing a large amount of information. The main task of innovative development of construction development is to create competitive advantages in the strategic perspective, which form a safe and comfortable environment for human life that meets high international quality standards, ensuring sustainable socio-economic development of the country's construction industry. Integrated Project Delivery (IPD) is a completely new idea based on Building Information Modelling (BIM) technology. The main idea is to work closely with the investor, designer and executors of a construction project. Achievement of competitive advantages should be based on innovative re-equipment of the construction industry, development of innovative competences, engineering schemes for life cycle management of construction projects, application of information technologies to increase productivity, reduce energy intensity, material consumption and cost of construction products. The article analyses the benefits of using information modelling and the integrated project implementation method in construction. A mathematical model for choosing the optimal network organisational structure of a project is developed. The main factors affecting the synergistic effect of the construction project implementation (costs of making changes to the project, increasing the implementation time, transaction and operating costs) are identified. An economic and mathematical model for assessing the synergistic effect of the joint implementation of integrated construction project implementation and information modelling has been built and tested in practice. A simulation model has been developed for a comprehensive study and optimisation of the communication network of participants in the implementation of a construction project. The results obtained will allow the top management of contractors to effectively monitor, structure and manoeuvre assets in their operations, substantiate the economic strategy and parameters of the production and business portfolio of construction companies.

Strategic Integration of Drone Technology and Digital Twins for Optimal Construction Project Management

Strategic Integration of Drone Technology and Digital Twins for Optimal Construction Project Management

A deep neural network algorithm-based approach for predicting recovery period of accidents according to construction scale

Despite ongoing safety efforts, construction sites experience a concerningly high accident rate. Notwithstanding that policies and research to reduce the risk of accidents in the construction industry have been active for a long time, the accident rate in the construction industry is considerably higher than in other industries. This trend may likely be further exacerbated by the rapid growth of large-scale construction projects driven by urban population expansion. Consequently, accurately predicting recovery periods of accidents at construction sites in advance and proactively investing in measures to mitigate them is critical for efficiently managing construction projects. Therefore, the purpose of this study is to propose a framework for developing accident prediction models based on the Deep Neural Network (DNN) algorithm according to the scale of the construction site. This study suggests DNN models and applies the DNN for each construction site scale to predict accident recovery periods. The model performance and accuracy were evaluated using mean absolute error (MAE) and root-mean-square error (RMSE) and compared with the widely used multiple regression analysis model. As a result of model comparison, the DNN models showed a lower prediction error rate than the regression analysis models for both small-to-medium and large construction sites. The findings and framework of this study can be applied as the opening stage of accident risk assessment using deep learning techniques, and the introduction of deep learning technology to safety management according to the scale of the construction site is provided as a guideline.

드론을 활용한 스마트감리시스템의 발전방향에 관한 연구

Currently, Korea's smart construction industry is becoming more advanced, and technology that can promote safety by incorporating ICT (Information & Communication Technology) convergence technology is being applied and developed. This study aims to provide information on the construction site to supervisors and builders by providing analysis data to the construction site using drones, etc., and to suggest development directions through smart supervision system improvement. In this study, after deriving major items through prior research, a focus interview was conducted with 7 supervisors to select important items for each research problem, and a total of 150 people, including construction project managers, manager supervisors, safety managers, and workers, were surveyed. As a result of the analysis of the survey on the satisfaction of smart supervision systems using drones at construction sites, the operation of drones will be highly useful in the construction field in the future (8.7, 25.92%), the smart supervision system using drones reflects what builders and supervisors want (8.3, 24.86%), and the drone is consistent with the goal of using smart supervision (8.3, 24.73%) and the drone system level is excellent (8.2, 24.49%). Through this study, it was found that the smart surveillance system using drones would be very useful in the construction field, and it was found that the efficiency of safety, accuracy, and economy could be improved by using drones instead of visually measuring and checking the field. It is hoped that the research results of this paper will provide the information necessary to improve the quality of the surveillance system in the construction sector at home and abroad and to prevent safety accidents at construction sites.

THE EFFECT OF BUILDING INFORMATION MODELLING (BIM) METHOD IMPLEMENTATION ON THE EFFECTIVENES OF BUILDING CONSTRUCTION IMPLEMENTATION MANAGEMMENT IN THE CONSTRUCTION INDUSTRY

The aim of this study is to analyze the impact of Building Implementation Information Modeling (BIM) on the efficiency of building construction management quantitatively. It has become a significant method in the construction industry with the potential to improve collaboration, coordination and information management in the implementation of construction projects. The research method uses simple linear regression analysis with data from construction professional respondents. The study involved 52 contractor companies in the construction industry. The selection of these samples is aimed at obtaining representative and adequate data for simple linear regression analysis. With large sample sizes, the results are expected to be more accurate and reliable. In addition, the selection of 52 respondents is also carried out to cover a variety of perspectives and professional experience of construction, making the data obtained more representative, and more reliable for the analysis performed. Implementation of BIM has a significant and positive impact on building construction management, demonstrated by a count t value = 12.217 greater than the table t value of 2.00856. The determination coefficient (R-square) of 0.749 or 74.9% suggests that 74.9 percent variation in building management efficiency can be explained by the implementation of BIM. This finding emphasizes the importance of using BIM to improve management effectiveness in building construction in the construction industry. The implications of this research provide a basis for encouraging the adoption of BIM as an effective tool in the management of construction projects.