Real Construction Project Research Articles

Moderating Effect of Project Environment on Financial and Contract Management Practices and Construction Cost Overruns in Real Estate Project

Understanding the complex relationship between a project's environment, financial governance, and community involvement is crucial for managing construction costs in real estate. This research work, therefore, seeks to identify the extent of moderation of project environment on the association of the financial and contract management practices with cost overrun in real estate construction projects. The results of the multiple linear regression analysis as well as the Pearson correlation coefficient were significant; this indicates that the project environment had a moderating effect on the relationship between financial and contract management practices and the occurrence of cost overruns in real estate projects. Future research should explore additional variables to gain a more comprehensive view of construction cost overruns.

Improving Project Estimates at Completion through Progress-Based Performance Factors

From a managerial perspective, project success hinges on estimates at completion as they allow tailoring response actions to cost and schedule overruns. While the literature is moving towards sophisticated approaches, standard methodologies, such as Earned-Value Management (EVM) and Earned Schedule (ES), are barely implemented in certain contexts. Therefore, it is necessary to improve performance forecasting without increasing its difficulty. The objective of this study was twofold. First, to guide modeling and implementing project progress within cost and to schedule Performance Factors (PFs). Second, to test several PFs utilized within EVM and ES formulae to forecast project cost and duration at completion. Progress indicators dynamically adjust the evaluation approach, shifting from neutral to conservative as the project progresses, either physically or temporally. This study compared the performance of the progress-based PFs against EVM and ES standard, combined, and average-based PFs on a dataset of 65 real construction projects, in both cost and duration forecasting. The results show that progress-based PFs provide more accurate, precise, and timely forecasts than other PFs. This study allows practitioners to select one or more of the proposed PFs, or even to develop one, following the guidelines provided, to reflect best their assumptions about the future course of project performance.

Automated quantification of construction workers’ exposure to struck-by falling object hazards

The construction industry is among the most hazardous industries, and its continuously changing and complex environment results in a labour-intensive task to plan and prevent hazards. The current manual safety planning procedures cannot keep up with the construction progress. This leads to unplanned durations and an increased responsibility for the individual workers to analyse and act accordingly to an emerging situation. This work proposes an automated approach to identify and measure the amount of struck-by falling objects hazard exposure to the construction tasks and their assigned work crews. Additionally, falling objects can originate from activities not foreseen or planned due to planning resolution (e.g., crane lift paths or temporary impassable access routes). Therefore, it is investigated how to extend the current practises of safety analysis in both the planning and construction stages using BIM artificial intelligence and sensor techniques. The proposed strategy is to identify hazard sources and subjects based on their topology and nature in a spatiotemporal analysis. The proposed combinatorial analysis approach is validated in the case study performed on a real construction project in Finland. It yields new insights, which can be necessary for construction sequence decisions and convince workers to improve their safety behaviour.

A multi-attribute decision making context for supply chain management using possibility single-valued neutrosophic soft settings

Argued decisions, risk estimation, empirical study, forecasts, apprehension supervision, and productive exposition depend on the degree of possibility. It enables us to assess the degree to which specific results are acceptable and to make more informed decisions by drawing on the information at our disposal and logic. Although some scholars have previously examined hybrid structures resembling fuzzy soft sets with possibility degree settings serving as fuzzy membership grades, the current study introduces an innovative structure that permits more adaptable and comprehensive settings: single-valued neutrosophic grades, to serve as possibility degrees. Thus, this study aims to introduce a new mathematical structure, i.e., possibility single-valued neutrosophic soft set (psv-NSOS), which combines three important theories (i.e., possibility theory, single-valued neutrosophic theory, and soft set theory). The basic notions, and set-theoretic operations, i.e., union and intersection, of psv-NSOS are investigated and manipulated with matrix representations. Considering evaluating suppliers for a real estate construction project as a multi-attribute decision-making issue, an algorithm that utilizes the matrix manipulations of proposed set-theoretic operations is presented. The suggested algorithm?s robustness is confirmed by comparison to evaluate its dependability and adaptability for modeling uncertainties related to the supplier selection problem.

Lean in the public sector in Finland.

Finland is undergoing an immense change in the execution of real estate development and construction projects. Since 2011 nearly 100 projects have already been launched in the country with the Integrated Project Delivery (IPD) principle. The total value of the projects is close to 7.3 billion euros. The principles of integration are currently applied especially to project management contracts, but also to other traditional contract models with fixed content or fixed price. Implementation of the IPD models has triggered a change in work culture and practices across the whole construction field. Focus within the traditionally somewhat quarrelsome and fragmented industry has now shifted towards cooperation and continuous improvement – both important Lean principles. Experience from the integration of contracting authorities and service providers has shown the advantages to be achieved. The contracting authority can utilize the know-how of different parties from the start of the project, as designers and builders are planning ways of execution together. A joint development phase leads to better planning and more accurate budgeting as well as better commitment of different parties. The use of IPD models has also served as a significant basis for introducing Lean principles and tools in Finland. Without IPD models, especially the customers' awareness of the benefits of Lean methods would have remained low. Furthermore, the sharing of risks and benefits encourages the execution of projects with stricter adherence to budget and schedule. So far, all completed IPD projects have achieved the target schedule and budget. Despite minor shifts in the cost targets of the projects, all contracting parties have been satisfied with the achieved results.

QUALITY VERIFICATION OF OVERALL PLANNING SATELLITE REMOTE SENSING IMAGES PRODUCT FOR REAL 3D CHINA CONSTRUCTION PROJECT

Abstract. The overall planning of satellite remote sensing image products is an important part of the real 3D China construction project and an important measure for the overall management of remote sensing images. During the two years of implementation of the project in 2021 and 2022, we have organized and completed 9 versions of 2-meter resolution satellite remote sensing image products, each of which covers the land territory of the whole country (not less than 95% in principle). Image products are widely used in natural resources investigation and monitoring, basic surveying and mapping, geological survey, natural resources supervision, law enforcement and other fields, which effectively improves the guarantee capability of surveying and mapping geographic information services. Taking the 2022 2-meter resolution integrated satellite remote sensing image product as the research object, this paper first introduces the highlights of the product itself, that is, timeliness, availability and reliability. then the product quality is studied from six aspects: spatial reference system, position accuracy, logical consistency, time accuracy, image quality and attachment quality. On this basis, according to the characteristics of different geographical regions, seasonal time series, topography and geomorphology, 100 representative verification samples were selected from the overall satellite remote sensing image products in 2022 for experiments, the distribution law of product quality characteristics was analyzed, and the main quality problems were summarized.

On-site workshop investment problem: A novel mathematical approach and solution procedure

In real-world construction sites, On-Site Workshops (OSW) are installed to accelerate construction activities and facilitate the material handling process. These temporary OSWs are cost-effective, leading to decreasing the material handling cost and project makespan, which indicates their important role as a part of a construction project. However, considering the OSW, which has not been addressed in the project scheduling problems, requires the construction site to have a space capacity constraint while considering the workshop size, availability level, and other project-related constraints. In the present work, by considering the OSWs, a real construction project scheduling problem is studied as a Multi-Mode On-Site Workshop Investment Problem with Tardiness (MOSWIPT) while finding the installation/dismantling time of the OSWs. Two new (linear) mathematical programming models are proposed for MOSWIPT. Next, due to the NP-hardness of the problem, an enhanced Genetic Algorithm (GA)-based metaheuristic with efficient problem-specific improvement rules as local search and effective crossover and mutation operators is proposed. Computational experiments show that the proposed method has solved most of the instances of the addressed problem to optimality and outperformed the existing metaheuristics, e.g., Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Finally, conclusions and suggestions for future studies are stated.

Risk Management Practices and Performance of Real Estate Construction Projects in Nakuru County, Kenya

Risk Management Practices and Performance of Real Estate Construction Projects in Nakuru County, Kenya

Evaluating the Relationship Between Global Stock Markets and Real Estate Markets Before and After the Coronavirus Pandemic

The relationship between the stock market and the real estate market has been the focus of several studies in the past. The nature of this relationship continues to remain ambiguous, with results varying according to a country’s economic condition and the level of co-integration between the two markets. This paper reviews the variables that influence the behavior of the stock market and the property market in the United States and the UAE. It examines whether these markets appear to be interlinked in these countries. To make the research more relevant, we have also reviewed the relationship between these two markets and how they have been influenced by the Coronavirus pandemic. As per our findings, the key variables impacting this relationship include the wealth effect, the percentage of real estate assets owned by publicly-traded companies, the news, the number of mortgage-backed securities available in the market, and the amount of risk associated with these securities. Besides this, initial public offerings (IPOs) and market liquidity also play a vital role. Regarding the Coronavirus pandemic, the primary impacts of this health crisis appear to be limited to the short-term. The most significant effects include a rise in stock market volatility, a decline in buying and selling activity, and delays in real estate construction projects in response to government restrictions and new Coronavirus-related deaths. Keywords: Real estate, stock market, coronavirus pandemic, the impact of the stock market, property market, real estate prices, share prices, COVID-19 pandemic

Localization of underground pipe jacking machinery: A reliable, real-time and robust INS/OD solution

Accurate positioning is important for guidance control in pipeline construction, but traditional approaches fail in GPS-denied environments. This paper addresses the underground localization problem for the pipe jacking machine by presenting an Inertial Navigation System (INS)/ODometry (OD) scheme. It is a loosely coupled navigation system that combines an INS and an OD to provide reliable, real-time, and robust location estimation in an enclosed and restricted space. The combination is achieved by a multi-sensor fusion algorithm to avoid the divergence of standalone INS. Compared with the traditional localization technologies, the advantage of the INS/OD scheme is that there is no performance degradation or service failure despite severe blockage of the thick soil layer due to the selection of dead reckoning-based sensors. Moreover, an Unbiased Finite Impulse Response (UFIR) filter serves as the fusion mechanism to improve the robustness against a prior knowledge of noise, considering unknown and time-varying noise statistics in practice. The INS/OD scheme is verified through a numerical simulation and employed on-site in a real underground sewage pipeline construction project. Despite the construction site being more than 10 meters deep underground, the INS/OD scheme continues to perform reliably and robustly in real-time, significantly benefiting the engineering guidance enterprise.

Vibration Control of Metal Spatial Structures With Tuned Mass Dampers

additional-mass-type vibration-control devices, especially TMDs, and confirm their control performance under seismic loadings by both analytical and experimental methods. Firstly, to effectively control spatial structures that have different vibration properties than multi-story frame structures, we propose a spatially distributed MTMD method. This method is characterized by controlling plural modes with closely spaced natural frequencies based on the robust property of the MTMD against variation of natural frequencies. Next, we propose a TMD-with-initial-displacement method to improve transient response. Then, we describe a shaking table test using TMD models and compare test results and analytical results. Lastly, we describe a method of applying a Houde damper system through a real construction project; the Eye of the Yellow Sea Pedestrian Bridge.

Construction schedule augmentation with implicit dependency constraints and automated generation of lookahead plan revisions

Revising lookahead plans is practiced in all construction projects to meet contractual deadlines, mitigate activity delays, and disentangle process bottlenecks. We present a new machine learning-based method –which we call Implicit Logic Checker (ILC)– to learn the implicit dependency constraints and the flexibility of construction schedule relationships using a Transformer language model architecture. We then leverage relationships’ flexibilities to provide a basis for creating optimal lookahead plan revisions to help mitigate the effects of unavoidable activity delays. We then deploy a Constrained Conditional (CC) model that applies learned construction planning knowledge to build revised lookahead plans and optimizes them for consistency and duration. Our ILC model is trained and tested on 35,332 manually labeled predecessor-successor relationships from eight real construction projects achieving an F1-Score of 91%. Similarly, the revised lookahead plans generated by the CC model on two case studies show reduced overall plan durations.

Modified dynamic opposite learning assisted TLBO for solving Time-Cost optimization in generalized construction projects

This study proposes an improved version of teaching learning-based optimization (TLBO) with a modified dynamic-opposition learning (MDOL) strategy, called modified dynamic oppositional learning TLBO (MDOLTLBO) to narrow the gap between research and practice for generalized time–cost optimization. Instead of opposite numbers, this study employs modified dynamic opposite points for the population initialization and generation jumping. This novel feature of the proposed algorithm is intended to diminish the randomness of the initial population and minimize the search effort required to achieve the optimal set of time–cost alternatives in the search space. An iterative-based varying weighting factor for MDOL is introduced to adjust the range of the search space with the aim of balancing the dynamicity and diversity of the algorithm. Hence, the MDOLTLBO algorithm can identify diversified and high-quality solutions based on non-dominated solutions. Four different case studies of real construction projects, ranging from 29 to 290 activities, were investigated to demonstrate the applicability of the proposed model. The experimental findings verify that the proposed algorithm is efficient, effective, and highly satisfying. Hence, the primary contribution of this study is MDOL approach that can successfully achieve high-quality solutions and good convergence speed for large-scale discrete time–cost optimization problems with generalized logical relationships.

Control, coordination, and adaptation functions in construction contracts: A machine-coding model

Extant literature lacks an accurate and efficient measurement of construction contracts. Drawing on the control, coordination and adaptation functions of contracts, this study aims to develop a machine-coding model that provides a fine-grained and efficient tool for measuring complexity of construction contracts. To achieve this, we first constructed a dataset by manually coding 17 standard contract forms and 35 representative contracts selected from real construction projects. The DeBERTa language model, pre-trained on massive text data in the Natural Language Processing (NLP) field, was selected and fine-tuned for machine coding, achieving a high accuracy of 77.69%. The model provides a valuable tool for scholars to enrich construction contract research and for practitioners to design appropriate contracts by balancing different contract functions. This study inspires future research in the application of state-of-the-art NLP techniques to contract analysis and management in the construction industry.

The characteristics of PM emissions from construction sites during the earthwork and foundation stages: an empirical study evidence

The bulk of the particulate matter (PM) emissions generated during construction projects are significantly released during the earthwork and foundation stages. To reduce and control these emissions, it is necessary to have reliable data on their characteristics. However, construction PM are poorly characterized because their composition depends on several factors (e.g., weather and reduction measures) and various on-site activities whose effects may interact. To address these challenges, a long-term quantitative empirical study using advanced statistical methods was performed on a real construction project during the whole earthwork and foundation stages. The upwind-downwind method was used to collect data on PM emissions throughout the earthwork and foundation construction process, and correlation analysis, paired samples t-test, and partial least squares regression (PLS) were used to analyze TSP, PM10, and PM2.5 emissions and their relationships with various influencing factors. The results showed that both earthwork and foundation constructions generate substantial PM emissions because there were differences with statistical significances in the PM levels measured upwind and downwind of the construction site. TSP and PM10 emissions correlated moderately with humidity and wind speed. However, temperature and atmospheric pressure did not correlate significantly with any of the measured emissions. The main activities responsible for PM emissions during the earthwork and foundation construction stages were hammer piling, waste stacking, and materials transportation. Water spraying was found to effectively reduce TSP and PM10 emissions, while the use of a fog cannon more effectively reduced PM2.5 emissions. Construction PM is an important source of atmospheric pollution in cities; the findings presented herein provide cornerstone and knowledge to guide efforts for reducing its impact.

Dynamic-Bayesian-Network-Based Project Cost Overrun Prediction Model

One common problem in the construction industry is project cost overrun. Cost overrun can have significant impacts on financial profitability, project completion, project quality, and stakeholder satisfaction. The average percentage of construction project overrun can vary widely depending on the project type, size, complexity, and location. Many approaches can be adopted to prevent or mitigate project cost overrun, and one of them is a more accurate cost estimate and prediction. Several studies on the construction project cost overrun estimation and prediction have been conducted based on historical data; nevertheless, each project has its project characteristics and cost trend. Real-time, project-specific cost data are more reliable for forecasting the cost trend of the project itself. There are many influence factors that may interdependently affect the construction project cost overrun. This paper proposes a real-time predict cost overrun risk prediction model based on the influence factors and their interdependence as well as the corrective actions if adopted. This study used a dynamic Bayesian network (DBN) to formulate problem architecture and to use the input–output hidden Markov method (I/O HMM) with particle filter (PF) to run inference. Six building and mass rapid transit projects in Taiwan were used as model validation and comparison. The posterior probabilities from the DBN-based cost overrun risk prediction model were highly consistent with the cost overrun ratios of real construction projects. Moreover, it is superior to other prediction models in terms of accuracy. The proposed model could provide project managers with an early alert for cost overrun.

Project Cost Overrun Risk Prediction Using Hidden Markov Chain Analysis

Construction project cost overrun is a common problem in the construction industry. The cost of construction projects is thought to have increased by approximately 33% on average. Several types of research on construction project cost overrun have been conducted and these generally rely on historical data. However, whilst each project has its own project characteristics and cost trend, real-time project cost data are more reliable to forecast its own cost trend. This paper proposes a real-time hidden Markov chain (HMM) model to predict cost overrun risk based on project-owned cost performance data and the corrective actions if adopted. The cost overrun events occurrence in this model was assumed to follow a Poisson arrival pattern. Real-time HMM with a particle filter was used to run the simulation. One SRC building project in Taiwan was used for model validation and comparison. The posterior probabilities from the real-time HMM model were highly consistent with the cost overrun ratios of real construction projects. The proposed cost overrun prediction model could provide an early alert of cost overruns to the project manager. Based on the survey of cost overrun risk and significantly influential factors, we propose effective cost management plans to alleviate the frequency of project cost overrun.

Multistage self-adaptive decision-making mechanism for prefabricated building modules with IoT-enabled graduation manufacturing system

Prefabricated production Planning, Scheduling, and Execution (PSE) is crucial for the on-time delivery of prefabricated modules in Modular Prefabricated Construction (MPC). In prefabricated production, various uncertainties decrease the effectiveness of production PSE, increasing construction periods and costs. However, a stable and resilient fixed-position production mode with high applicability that meets the characteristics of prefabricated production has yet to be realized. In the context of Industry 4.0, a Graduation Intelligent Manufacturing System (GiMS) is described to facilitate the flexible handling of uncertainties in real time. Internet of Things (IoT)-enabled prefabricated production is designed to achieve real-time visibility and traceability. A multistage self-adaptive decision-making mechanism comprising spectral clustering for planning and real-time ticket sequencing for scheduling and execution is developed to improve the performance levels of PSE decisions under large fluctuations in operation time. A numerical study is conducted to evaluate the performance of the GiMS. According to the results, >70% improvement in tardy job reduction and a 16.1% average decrease in mold table idle time are achieved relative to two traditional methods. Sensitivity analysis is carried out to investigate the impacts of fluctuations in the operation time and the number of mold tables. GiMS possesses excellent potential to cope with uncertainties, needing up to four mold tables for modular prefabricated production. This proposed methodology fills the gap in fixed-position prefabricated production PSE and increases the applicability of the decision-making mechanism in real construction projects.

Using the ordinal priority approach for selecting the contractor in construction projects

Abstract Effective decision-making process is the basis for successfully solving any engineering problem. Many decisions taken in the construction projects differ in their nature due to the complex nature of the construction projects. One of the most crucial decisions that might result in numerous issues over the course of a construction project is the selection of the contractor. This study aims to use the ordinal priority approach (OPA) for the contractor selection process in the construction industry. The proposed model involves two computer programs; the first of these will be used to evaluate the decision-makers/experts in the construction projects, while the second will be used to formulate the OPA mathematical model. The experts’ interview was used to identify the criteria of evaluation process of the decision-makers/experts, while Delphi survey with principal component analysis (PCA) was conducted to identify the required selection criteria of the construction projects contractors. The results illustrate that there are 20 criteria for selecting the construction contractor, and 7 criteria for evaluating the decision-makers/experts in the construction projects. Finally, the proposed model has been applied in a real construction project, and showed good results.

Systemic Quality Management Processes in Construction Projects

This research is part of the quality approach of a construction company. It aims to make the assessment of quality management for a real construction project, this assessment will be done through the analysis of the database of the company and the documentation established during the realization of the site. Obtaining quality in construction will always remain problematic, in the absence of monitoring of construction work, defects in the execution may appear and affect the durability, stability, and aesthetic appearance of the built work. The non-quality can affect the project from the design and study phase, through the course of the work to the commissioning of the project. Hence, the need for quality control in the successive phases of design and construction, an analysis of the issues, and a description of the quality system can formalize the requirements of sustainable adequacy of works to ensure that the project requirements are constantly met according to the ISO 9001 and Moroccan standards of construction. A formalized risk management tool for non-conformities previously encountered on site is implemented to put into practice the theoretical and methodological approach proposed in several case studies. The current study develops an operational quality management process for the company, to identify non-conformities, qualify, and quantify them. This tool could become a powerful communication tool for all actors and a real decision support tool within the companies. Indeed, complex construction projects present enriching experiences to better master the economy, technique, design, construction, and maintenance. The future of this type of innovative projects depends on better collaboration between the parties to share responsibilities and risks. Keywords: quality management, construction site, non-compliance, formalized process, ISO 9001. https://doi.org/10.55463/issn.1674-2974.50.2.28