Monitoring Of Construction Projects Research Articles

Factors affecting implementation of computer vision-based technologies adopted for monitoring buildings construction projects

Construction monitoring in dynamic construction site environments poses significant challenges for construction management. To overcome these challenges, the implementation of computer vision (CV) technologies for construction project monitoring has gained traction. This study focuses on investigating the factors influence the successful implementation of CV technologies in monitoring construction activities within building projects. A comprehensive methodology was employed, including a systematic review of CV technologies implemented in construction and qualitative surveys conducted with construction experts. Additionally, a quantitative questionnaire was developed, and the collected data was analysed using structural equation modelling. The findings reveal the presence of 10 factors categorized into four constructs. Notably, all 10 factors demonstrate high value factor loadings and statistical significance, and among the four constructs (device, jobsite, environment, human), device (0.82) has the highest impact on the implementation of CV-based technologies on the construction site, followed by jobsite condition (0.62), human (0.61), and environment (0.51) came in the last place. By addressing these influential factors and mitigating their effects, construction stakeholders can enhance the implementation of CV technologies for monitoring construction sites. This study contributes valuable insights that inform the implementation and optimization of CV technologies in construction projects, ultimately advancing the field of construction management.

Copula: a decentralized solution for construction project monitoring using blockchain

PurposeThis study aims to introduce and evaluate the COPULA framework, a construction project monitoring solution based on blockchain designed to address the inherent challenges of construction project monitoring and management. This research aims to enhance efficiency, transparency and trust within the dynamic and collaborative environment of the construction industry by leveraging the decentralized, secure and immutable nature of blockchain technology.Design/methodology/approachThis paper employs a comprehensive approach encompassing the formulation of the COPULA model, the development of a digital solution using the ethereum blockchain and extensive testing to assess performance in terms of execution cost, time, integrity, immutability and security. A case analysis is conducted to demonstrate the practical application and benefits of blockchain technology in real-world construction project monitoring scenarios.FindingsThe findings reveal that the COPULA framework effectively addresses critical issues such as centralization, privacy and security vulnerabilities in construction project management. It facilitates seamless data exchange among stakeholders, ensuring real-time transparency and the creation of a tamper-proof communication channel. The framework demonstrates the potential to significantly enhance project efficiency and foster trust among all parties involved.Research limitations/implicationsWhile the study provides promising insights into the application of blockchain technology in construction project monitoring, future research could explore the integration of COPULA with existing project management methodologies to broaden its applicability and impact. Further investigations into the solution’s scalability and adaptation to various construction project types and sizes are also suggested.Originality/valueThis research offers a comprehensive blockchain solution specifically tailored for the construction industry. Unlike prior studies focusing on theoretical aspects, this paper presents a practical, end-to-end solution encompassing model formulation, digital implementation, proof-of-concept testing and validation analysis. The COPULA framework marks a significant advancement in the digital transformation of construction project monitoring, providing a novel approach to overcoming longstanding industry challenges.

IMPLEMENTATION OF A GEOGRAPHIC INFORMATION SYSTEM IN MONITORING THE IMPLEMENTATION OF HOUSING CONSTRUCTION

Building the 864-unit housing complex was a significant construction undertaking. Within an implementation period of 18 months and a project area of approximately 34 hectares, this project demanded efficient management to control the time and budget that had been set. To achieve this goal, it is necessary to implement effective planning, organization, management and resource allocation processes. This process aims to break down project management into simpler tasks or activities, provide time estimates for each task, and continuously monitor their progress. Although conventional planning tools such as Primavera and Msproject can provide a time representation of the tasks to be performed, they are not yet able to provide spatial or geographic monitoring of these tasks within the scope of the project. By integrating Geographic Information Systems (GIS), this project can gain additional benefits. GIS allows identifying data about the progress of construction tasks, and then presenting them in the form of maps. This not only visualizes overall progress graphically and geographically, but also makes it easy to extract useful summaries to support project decision making. With GIS, construction project monitoring not only becomes more efficient, but also more up-to-date and relevant to the scope of the project involving various dispersed structures. It is hoped that GIS integration can improve project management control and optimize the final results of the construction of the 864 housing complexes.

Unlocking the full potential of reality capture for construction project cost monitoring: a mixed review

Purpose Digital data acquisition is crucial for operations in the digital transformation era. Reality capture (RC) has made an immeasurable contribution to various fields, especially in the built environment. This paper aims to review RC applications, potentials, limitations and the extent to which RC can be adopted for cost monitoring of construction projects. Design/methodology/approach A mixed-method approach, using Bibliometric analysis and the PRISMA framework, was used to review and analyse 112 peer-reviewed journal articles from the Scopus and Web of Science databases. Findings The study reveals RC has been applied in various areas in the built environment, but health and safety, cost and labour productivity monitoring have received little or no attention. It is proposed that RC can significantly support cost monitoring owing to its ability to acquire accurate and quick digital as-built 3D point cloud data, which contains rich measurement points for the valuation of work done. Research limitations/implications The study’s conclusions are based only on the Scopus and Web of Science data sets. Only English language documents were approved, whereas others may be in other languages. The research is a non-validation of findings using empirical data to confirm the data obtained from RC literature. Practical implications This paper highlights the importance of RC for cost monitoring in construction projects, filling knowledge gaps and enhancing project outcomes. Social implications The implementation of RC in the era of the digital revolution has the potential to improve project delivery around the world today. Every project’s success is largely determined by the availability of precise and detailed digital data. RC applications have pushed for more sustainable design, construction and operations in the built environment. Originality/value The study has given research trends on the extent of RC applications, potentials, limitations and future directions.

Assessing the Impact of Unmanned Aerial Systems on Project Site Safety and Accessibility: A Comprehensive Study

The construction industry, responsible for shaping the modern world through iconic structures and critical infrastructure, faces a troubling challenge – a high frequency of occupational accidents. These accidents not only imperil workers’ lives but also impose significant economic and environmental burdens. This paper delves into the multifaceted realm of construction site safety and risk management, with a particular focus on the risks associated with construction project monitoring. It explores existing literature, emphasizing the role of organizational management, worker involvement, safety systems, and cultural factors in fostering a safety-oriented environment. Additionally, it reviews studies from various countries, shedding light on diverse factors affecting construction site safety. Beyond human factors, the paper highlights risk inherent in construction projects, emphasizing the need for proactive risk management. The paper concludes by acknowledging the gravity of construction site accidents and their far-reaching consequences, underscoring the importance of holistic risk management strategies.

Challenges Faced During Implementation of Digital Twin in Construction Project Monitoring

Digital Twins (DTs) are gaining popularity because they provide precise digital copies of assets, processes, and systems. This is especially true when these DTs are paired with real-time simulation models that make use of modern technologies like machine learning, artificial intelligence, and data analytics. These combinations can provide a comprehensive and dynamic view of the monitored systems. Digital twin (DT) has shown tremendous potential to bring about revolutionary improvements in the field of construction site surveillance. There is, however, a notable paucity of empirical research identifying the constant elements affecting DT adoption in this industry. This research tries to fill that void by identifying the important elements that determine the usage of DT in construction. The study adopts a complete framework with the goal of increasing the use of DT in building site monitoring. The elements influencing the adoption and effectiveness of distributed ledger technology (DT) are divided into three categories: technological, organizational, and economic. Technological factors include the system's appropriateness and the robustness of the data infrastructure. Organizational considerations include the company's openness to innovation and leadership support. Economic aspects include things like return on investment (ROI) and cost-effectiveness. The research technique combines case studies and literature reviews to examine the benefits and drawbacks of DT in construction monitoring. This study's expected output is a comprehensive framework that aids construction businesses in optimizing the use of DT in site monitoring. This would allow for more efficient, data-driven, and forward-thinking processes. The study's ultimate purpose is to provide critical knowledge that will assist the building sector in adopting cutting-edge methods. The industry may better plan for the integration of this sophisticated technology into their operations by knowing the potential of DT and the variables driving its adoption. This, in turn, can lead to more efficiency, lower risks, and improved overall performance

A Comparison of the Project Management Methodologies PRINCE2 and PMBOK in Managing Repetitive Construction Projects

Nowadays, companies employ various project management (PM) methodologies to ensure that their projects are effective and successful. It is worth knowing that differences in principles and processes of PM methodologies influence the use of different PMs in managing non-repetitive and repetitive construction projects. This paper presents the selection and application of a rational construction PM methodology to a repetitive construction project after a comparison of two PM methodologies, namely Project Management Body of Knowledge (PMBOK) and Projects IN Controlled Environments (PRINCE2). The object of this study is a repetitive anti-corrosion works project for steel structures conducted at Company X. The research was carried out in two steps. First, a quantitative survey of the respondents from companies involved in the management and execution of construction projects was conducted with the aim to identify a rational approach to construction PM. The questionnaire consisted of fourteen closed-ended questions, six of which were generic and eight were PMBOK- and PRINCE2-specific questions. Companies that took part in the quantitative study identified the PRINCE2 project management approach as the most suitable for managing a repetitive construction project. Using the PRINCE2 PM methodology, the repetitive construction project would aim to provide as much information as possible to the project participants, form a team and assign team leaders responsible for the phases, establish a financial plan, a detailed timetable for the execution of the works, a quality control plan, and a plan of responsible persons, and detail the technological sequencing of the works. Second, a quantitative study on the selection of a rational construction project management approach for a repetitive construction project was pursued, and a qualitative assessment of construction project monitoring trends and actions was conducted. The qualitative research was performed using a structured interview method and asking the representatives of different companies X, Y, and Z the same 15 questions. The results of the qualitative research showed that a successful PM depends on the size of the project team, the PM tools and methodologies used, the PM philosophy, and the frequency of monitoring and discussing the project progress.

BLOCKCHAIN OF OPTIMAL MULTIPLE CONSTRUCTION PROJECTS PLANNING UNDER PROBABILISTIC ARRIVAL AND STOCHASTIC DURATIONS

With the rapid development of projects, firms are facing challenges in planning and controlling complex multiple construction projects. This research, therefore, aims at developing blockchain of optimal scheduling and sequencing of multiple construction projects under probabilistic arrival and stochastic durations. Each project task was considered as a block. Then, a framework for electronic project recording (EPR) system was developed. The EPRs are records for project tasks that make information available directly and securely to authorized users. In this framework, two optimization models were developed for scheduling and sequencing project blocks. The scheduling model aims to assign project tasks to available resources at minimal total cost and maximal the number of assigned project tasks. On the other hand, the sequencing model seeks to determine the start time of block execution while minimizing delay costs and minimizing the sum of task’s start times. The project arrival date and the task’s execution duration were assumed probabilistic and stochastic (normally distributed), respectively. The developed EPR system was implemented on a real case study of five projects with total of 121 tasks. Further, the system was developed when the task’s execution duration follows the Program Evaluation and Review Technique (PERT) model with four replications. The project costs (idle time and overtime costs) at optimal plan were then compared between the task’s execution duration normally distributed and PERT modelled. The results revealed negligible differences between project costs and slight changes in the sequence of project activities. Consequently, both distributions can be used interchangeably to model the task’s execution duration. Furthermore, the project costs were also compared between four solution replications and were found very close, which indicates the robustness of model solutions to random generation of task’s execution duration at both models. In conclusion, the developed EPR framework including the optimization models provided an effective planning and monitoring of construction projects that can be used to make decisions through project progress and efficient sharing of project resources at minimal idle and overtime costs. Future research considers developing a Blockchain of optimal maintenance planning.

Construction Schedule Management System for Large-Scale Construction Projects Based on Multisensor Network.

As an important task of construction project management, construction schedule management is related to the realization of project period, cost, quality, and other objectives. However, in actual construction, problems such as difficulties in plan implementation and management are often encountered, and schedule delays often occur. Moreover, with the increase in the scale and complexity of modern buildings, the management of construction schedules has brought about larger issues and higher requirements have been put forward for the management level of the construction schedule. The traditional timetable management and scheduling is not highly relevant to the project entity. There is no entity change, the project information loss is serious, the tracking and control of the construction timetable is not in place, there is fine management, and the construction timetable management cannot meet the needs of modern construction. In order to ensure the smooth implementation of projects and objectives, it is urgent to explore new management ideas and methods to promote the smooth progress of construction process management. BIM technology has the advantages of visibility, simulation, adjustment, optimization, and advanced data integration, which helps to make up for the shortcomings of previous schedule management and improve the level of construction schedule management. Based on the research situation of large-scale construction project monitoring system and the analysis of information fusion technology, this paper proposes a large-scale construction project monitoring system scheme based on multisensor network information fusion. Through the investigation of the monitoring content, the composition of the multisensor network is determined. This paper is applicable to large-scale construction projects. Through the research and design of hardware modules, software modules, and fusion algorithms, the data acquisition, transmission, calculation, and display functions of the monitoring system based on multisensor network information fusion are realized, which can effectively monitor the entire process of large-scale construction projects.

Automated progress monitoring of construction projects using Machine learning and image processing approach

Project progress tracking and controlling is one of the most critical responsibilities of construction project management. Current practices for forecasting the effectiveness of a construction project include an evaluation that is still laborious, long-drawn-out, and can prone to errors. Digitalization has a tremendous potential to enhance management practices in the field of construction. The building sector has been reluctant to admit new technology. This points out the importance of an automated method for tracking the progress of projects. The goal of this research is to implement a monitoring system for Construction Progress Tracking (CPT). Machine learning and image processing techniques are used to ensure CPT automatically. In particular, masonry activity in construction is focused, though it can be broadened to other construction work. The proposed deep learning model is trained using a set of data of 356 images taken from several worksites and websites. The proposed system is demonstrated with OpenCV Python language and an automated progress report is generated in Microsoft excel. The study developed a platform for automated building project progress monitoring. It has also built a systematic strategy for gathering the standardized dataset for benchmarking purposes. The following are some of the study's notable findings: A supervised CNN classifier was constructed for brickwork recognition using spatial and color information. The classifier worked well, with accuracy and recall values of 81% and 83%, respectively. A uniform collection of 356 photos was created. This resulted in the creation of a benchmarked dataset for a masonry wall. The achieved results reveal high precision and speed to effectively detect the construction project without the interference of human activities, thereby facilitating improved inspection and supervision.

Construction Project Monitoring: The Cost and Schedule Control by Earned Value Method (EVM)

On account of ever increasing complexities and uncertainties, construction project management has become more and more challenging day by day. As a result, continuous project monitoring emerges as a prime concern for the planned activities to be completed on schedule and within budget. Limitations in traditional time and cost monitoring practices result in time and cost overrun which seriously hinder successful completion of a project. The impact of time and cost at various stages of a construction project is a critical factor in the project's successful completion and management. Most of the construction projects in Bangladesh have completed with extra time which leads the demand of additional cost because of poor project monitoring and controlling system. In these consequences, a widely recognized emerging project monitoring technique, Earned Value Management (EVM) can be introduced to conduct project progress, performance and forecasting. EVM measures the actual work completed on a project at any time with respect to the original cost and schedule by integrating project scope, cost and schedule in order to predict cost and completion timeline. In this study, a three-storied residential building project at Khulna, Bangladesh was considered to perform earned value management for tracking whether the project is either delay or not along with either over budget or under budget at any particular day. The construction operations up to the first six months were assessed using an EVM based on a work-breakdown structure (WBS), in which the real activities and costs were compared to the projected program. The progress of the under constructed project considering cost and schedule were also evaluated through analyzing EVM indexes using Microsoft Project Professional 2019 software. Construction performance was assessed respecting cost variance (CV), schedule variance (SV), cost performance index (CPI) and schedule performance index (SPI). CV and CPI are evidently less than 0 and 1, respectively, indicating thatthe project was on the verge of cost overrun; on the other hand, SV and SPI are both less than 0 and 1, indicating that the project was running behind budget after six months. As a result, if this project is pursued at its current pace, it will be finished behind schedule and above budget. It will undoubtedly assist project rescheduling to bring the performance back to harmony.

Advanced Sound Classifiers and Performance Analyses for Accurate Audio-Based Construction Project Monitoring

AbstractThe sounds of work activities and equipment operations at a construction site provide critical information regarding construction progress, task performance, and safety issues. The construc...

Text mining-based construction site accident classification using hybrid supervised machine learning

Safety is one key consideration in the monitoring of construction projects by engineers. Accidents in the project can potentially cause issues, such as workers' injury and progress delay, which lead to financial losses. Generally, accident narratives store all summaries and causes of the related events. Since documentations rapidly use large quantities of resources, the implementation of Artificial Intelligence (AI) begins to seek attention. Nevertheless, in current models, there are still drawbacks, such as weak learning performance and substantial error rate. In this regard, this study develops a hybrid model incorporating Gated Recurrent Unit (GRU) and Symbiotic Organisms Search (SOS), named Symbiotic Gated Recurrent Unit (SGRU). SOS searches the best parameters of GRU to ensure optimal performance. Furthermore, Natural Language Processing is applied to pre-process the text data prior classification process. The experimental result in this study showcases SGRU as the best classification model among other AI models. Therefore, SGRU shares the capability to aid the safety assessments of construction projects.

On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning

While unavoidable, inspections, progress monitoring, and comparing as-planned with as-built conditions in construction projects do not readily add tangible intrinsic value to the end-users. In large-scale construction projects, the process of monitoring the implementation of every single part of buildings and reflecting them on the BIM models can become highly labour intensive and error-prone, due to the vast amount of data produced in the form of schedules, reports and photo logs. In order to address the mentioned methodological and technical gap, this paper presents a framework and a proof of concept prototype for on-demand automated simulation of construction projects, integrating some cutting edge IT solutions, namely image processing, machine learning, BIM and Virtual Reality. This study utilised the Unity game engine to integrate data from the original BIM models and the as-built images, which were processed via various computer vision techniques. These methods include object recognition and semantic segmentation for identifying different structural elements through supervised training in order to superimpose the real world images on the as-planned model. The proposed framework leads to an automated update of the 3D virtual environment with states of the construction site. This framework empowers project managers and stockholders with an advanced decision-making tool, highlighting the inconsistencies in an effective manner. This paper contributes to body knowledge by providing a technical exemplar for the integration of ML and image processing approaches with immersive and interactive BIM interfaces, the algorithms and program codes of which can help replicability of these approaches by other scholars.

Monitoring graditeljskog projekta

Monitoring graditeljskog projekta

Selection of tachymetry spacing in the process of monitoring of construction projects

The urbanization of territories and the increase in density of urban development cause the necessity of introduction of improved structural solutions into the construction practice. This is also connected with both the erection of higher buildings with longer span structures and the use of non-standard methods for the analysis of structures. The introduction of modern structural patterns lessens considerably the weight of structures, reduces the consumption of materials and cuts the construction production costs. At the same time, the responsibility for the construction projects enhances. A systematic control over the state of structures including a quasi-continuous one, allows us to reveal the very beginning of destructive processes and to take measures for their liquidation. One of monitoring methods is the tachymetry survey of positions of a number of adjusting marks fixed at the structural elements. The non-reflection mode of operation of tachymetry survey allows lifting the restrictions for the number of points under observations. The combination of the afore-said factors determines the urgency of the use of the tachymetry as a tool for monitoring the state of the construction project. The subject of the study: the subject of the present research work is the methodology of selection of tachymetry spacing during the deformation monitoring of a construction project. The tachymetry can be carried out both in the mode of focusing on pre-established marks, and in the non-reflection mode through the points on the structure. The disadvantage of the first method is the need of installation of light-reflecting marks, which is not always possible due to some technical and/or aesthetic reasons and may lead to a significant increase in the cost of monitoring. The disadvantage of the second method is a reduced accuracy of the measurements. A wide incremental step may lead to the failure of detection of deformation processes, a narrow step means a considerable increase in the monitoring time and an unjustified rise in the cost of monitoring. Objectives: the purpose of this research work is the optimization of tachymetry spacing, which will reveal all deviations of structural elements from their permanent positions by a value exceeding the accuracy of measurements. Materials and methods: the initial material for the study included the results of geodetic observations carried out at various construction projects, in particular, the tachymetry results. The method of study includes the comparing of the limiting admissible curvature value to the minimum deformation value measured with the tachymeter accuracy. Results: a methodology is suggested for the selection of the tachymetry survey step. On its basis, a formula for the determination of a step value is offered that takes into account the geometry of the structure, the strain capacity of the material and the accuracy of the survey. Conclusions: the obtained results allow us to optimize the number of the observation points during the tachymetry survey and to ensure the detection of all destructive effects associated with structural geometry changes at the construction project. The descriptions of the methodology are recommended for their application in the development of geodetic monitoring programmes.

Evaluation of UFPA quality assessment criteria for monitoring and evaluation system in the Ghanaian construction industry

The need to ensure that construction project monitoring and evaluation systems generate the required project information for effective project delivery and decision-making is imperative for project success. The study evaluates the UFPA quality assessment criteria for monitoring and evaluation system in the Ghanaian construction industry. The study employed the use of a structured questionnaire for data collection from forty (40) purposively selected construction industry professionals practising as consultants and contractors. Data collected were analysed using the Statistical Package for Social Sciences (SPSS). The mean item score (MIS) and analysis of variance (ANOVA) were used to compute the mean ranking of the quality assessment criteria and to compare the difference of means amongst the various groups and the assessment criteria. The findings revealed, in order of importance, accuracy, feasibility, propriety and utility as significant in influencing the quality of M&E systems in the Ghanaian construction industry. The analysis of variance also revealed no significant difference between the response of the two groups of respondents: consultants and contractors. The study recommends the adoption of the UFPA model as an essential quality assessing criterion for construction project M&E systems in the Ghanaian construction industry to enable effective M&E of projects.

Practice Device Artificial Improvement Basis of Soil Technologies Jet Grouting

To ensure the mechanical safety “base + foundation + building” system can control all its components. One of these techniques is to design the control device and artificially improved grounds with the required mechanical properties. This problem is perspective in those cases where the base building condition is very poor, and the payment of their impact modification of other components of the system is impractical or economically inefficient. This approach has been implemented by “New Ground” on more than 300 construction sites in the Perm region, Moscow, St. Petersburg, Krasnodar, Samara, Ufa, Tyumen, Kazan. Geotechnical monitoring of construction projects carried out jointly with specialists of the Perm National Research Polytechnic University, Institute of Continuum Mechanics and Mining Institute (Ural Branch of the Russian Academy of Sciences). Practical experience in this kind of artificial bases proposed for discussion in the framework of the proposed report.

AN EFFECTIVE DATABASE MANAGEMENT OF THE URBAN UNDERGROUND FACILITIES AND TOPOGRAPHIC INFORMATION

In developed countries the most accurate and reliable spatial data (M 1:500–1:1000) are extensively used. The same spatial object could be inscribed into separate systems: utilities companies have some systems, municipality collected catalogues of CAD data, separately developed real estate map. The same spatial objects are repeated and reiterated, unreasonable costs are incurred for their storage, the customers find it complicated to determine and select the best and appropriate spatial data required just for their own needs. This article suggests the solution of the problem. Authors have introduced the idea to compose and develop information system specialized for topography and infrastructure subsequently to be merged and coordinated. The model for data storage, data provision to the customers and service activities is defined. The available electronic services for the spatial data are described as advantageous for implementation on topographic maps, for inspection and monitoring of construction projects, for the issue of permits of excavation works and for the other activities.

Reactive scheduling based on actual logistics data by applying simulation-based optimization

Abstract Background A reasonable management and monitoring of construction projects requires accurate construction schedules. Accuracy depends highly on availability of reliable actual logistics data. Such data contain information about available material, equipment, personnel, updated delivery dates, and other data on site conditions. However, such data is often associated with different types of uncertainties due to infrequent collections, varying transport times, or manual assessments. Nonetheless, consideration of these uncertainties is important for evaluating actual data regarding their impact on the overall construction progress. Currently, the integration of such data into construction schedules is a time-consuming, manual and, thus, error-prone process. Therefore, in practice schedules are not updated as often as they should be. Methods To ease the handling of actual data and their integration into construction schedules, a reactive construction scheduling approach is presented. The approach is structured into four successive steps. To evaluate and systematically analyze uncertain actual data, fuzzy set theory and α-cut method are incorporated. Thus, actual data can be integrated into discrete-event simulation models. These models are used to perform simulation-based sensitivity analyzes, which evaluate impacts on construction schedules. As a result, an actual schedule is generated, such that a target-actual schedule comparison can be performed. If significant deviations or problems are identified, adaption is necessary and a new schedule needs to be generated. Thereby, different restrictions on the target schedule, such as contracted delivery dates, milestones or resource allocation must be considered. To perform this required adaption simulation-based optimization is utilized. Results To validate the method and show its advantages, an initial construction schedule example is created. The example is extended to incorporate uncertain actual logistics data. The proposed method shows how efficient actual data can be analyzed to update construction schedules. Further, the results show a competitive adaption of invalid construction schedules, such that contracted milestones, or other project objectives can be achieved. Conclusion The presented reactive construction scheduling method has the ability to improve current treatment of uncertain actual logistics data. This helps construction project managers to improve the management and monitoring of construction projects by reducing the time-consuming, error-prone process of updating inconsistent schedules.