Large-scale Construction Projects Research Articles

Urban Wastewater Management Using BIM Method

Building Information Modeling (BIM) is a method that emphasizes object-based data management, communication, and collaboration among all project participants throughout the entire lifecycle of a system. It is becoming an international standard for large-scale construction and infrastructure projects. Although BIM is gaining traction in the wastewater industry, the literature on its application in this field is still limited. This research, through expert interviews and literature review, explores the extent to which BIM has been introduced in the sewage industry and the associated opportunities and challenges. Initial projects in wastewater management using BIM include the construction and development of sewage treatment plants and special structures. Planning offices are leading the way in adopting BIM, supported by catalogs of industry-specific objects, clear definitions of responsibilities, and the potential for enhanced operation of sewage systems. Some users have already recognized the benefits of BIM and adjusted their processes accordingly. BIM’s object-oriented modeling improves the visualization of construction projects and processes, offering significant advantages in model-based plan derivation and mass determination. It also focuses on the collection, exchange, and storage of relevant data, which can benefit the entire wastewater industry. The research confirms that BIM adds value to wastewater management, especially since infrastructure is often difficult to access post-construction and knowledge about the systems is typically limited to those involved in the construction phase. However, to fully and effectively utilize BIM, further improvements are necessary. This requires intensive collaboration among project participants, software producers, and customers. The integration of BIM in wastewater management holds promise, but its successful implementation depends on overcoming current challenges and enhancing the method through collective effort. DOI: https://doi.org/10.52783/pst.928

A Mesoscopic Approach for the Numerical Simulation of a Mass Concrete Structure Construction Using Post-Cooling Systems

This study introduces an innovative numerical approach to simulate the construction of large concrete structures incorporating post-cooling systems employing the finite element method (FEM). The proposed methodology integrates critical construction parameters, including temperature control mechanisms, while accounting for concrete hydration and environmental conditions. Compared to traditional discrete models, this approach provides similar accuracy with substantially reduced computational costs, enhancing predictive capabilities in the thermal analysis of mass concrete. The method was applied to simulate the construction of a water intake pillar at the Tocoma hydroelectric plant in Venezuela, where the simulated results closely matched in situ temperature measurements. The findings highlight the method’s efficiency and accuracy in simulating post-cooling systems, offering a practical solution for improving the safety and cost-effectiveness in large-scale concrete construction projects.

The efficient waste-based fine-grained fibre concretes for 3D printing

This study investigates the development of optimized 3D printing mixes using Portland cement grades PC 400-D0 and PC 500-D0, combined with fly ash and various fibers such as polypropylene (PP), polyethylene (PE), and nylon, to enhance the mechanical properties and durability of printed structures. The primary aim is to create a mix that ensures sufficient flowability during the printing process while achieving high tensile strength and resistance to cracking. The methodology involved a series of mix designs with varying proportions of cement, fly ash, and fibers, followed by testing for rheology, compressive strength, and durability. Key findings include achieving a compressive strength of up to 50 MPa and a tensile strength improvement of 20 % with the optimal fiber combination. The incorporation of fly ash resulted in a 15 % reduction in material costs and a significant improvement in workability. The practical implications of this research are substantial for the construction industry, particularly in the context of sustainable and efficient building practices. The optimized mix designs not only reduce costs but also enhance the longevity and structural integrity of 3D printed elements, making them viable for large-scale construction projects. This study provides a foundation for further exploration into advanced 3D printing materials.

The Impact of Economic Fluctuations on Project Management Practices in Large-Scale Construction Projects

The Impact of Economic Fluctuations on Project Management Practices in Large-Scale Construction Projects

Exploring the rheology characteristics of Flowzan and Xanthan polymers in drilling water based fluids at high-temperature and high-shear rates

The study of drilling fluid behaviour across various temperature conditions is of paramount importance for industries such as oil and gas extraction, mineral exploration, and large-scale construction projects. To address this need, a custom-designed viscometer known as the Australian University of Kuwait Taylor-Couette System (AUTCS) was developed. The AUTCS apparatus incorporates a Taylor-Couette system, consisting of two co-axial cylinders with a testing liquid capacity of 100 ml. The inner cylinder can be rotated at speeds ranging from 0 to 3000 rpm. This configuration provides an economical testing system that is particularly suitable for studying expensive drilling fluids. To investigate high-temperature (HT) conditions, the apparatus is equipped with a controllable heating film jacket capable of warming fluids up to 350 F. One specific area of interest in the drilling industry is the exploration of less commonly used polymers, such as Flowzan and Xanthan, in water-based mud (WBM). Flowzan has gained increasing attention due to its potential applications in drilling fields. To facilitate the study of Flowzan and Xanthan, the AUTCS viscometer was fine-tuned using the standard conventional lab viscometer, FANN, that has limitations in terms of rotational speed range (0–600 RPM) and temperature range. The rheology characteristics of Flowzan and Xanthan including consistency charts, viscosity, and darcy friction factor are obtained and presented at temperatures of 79 F, 104 F, and 150 F, across a range of speeds from 0 to 3000 RPM. The findings were examined across several correlation functions. It is found that the Herschel–Bulkley model best represent Flow Zan and Xanthan polymers. These correlations contribute to a comprehensive understanding of the new polymers’ performance under different temperature and speed conditions in drilling applications.

Utilizing Project Management Techniques to Model Critical Success Factors in Large-Scale Construction Projects

This study investigates the application of project management techniques to model critical success factors (CSFs) in large-scale construction projects, utilizing Confirmatory Factor Analysis (CFA) to validate the measurement model. Through factor level analysis, eight distinct categories of CSFs were identified, highlighting their significance in project management success. The structural model analysis revealed positive correlations between project management success and CSFs, impacting project-level outcomes such as customer satisfaction, team satisfaction, and project profitability. Strong predictive validity and appropriate model fit were demonstrated through R-squared and Q-squared values, as well as WarpPLS analysis. The study's theoretical implications include a clear differentiation between project management success and project success, while practical implications underscore the importance of leveraging success factors to enhance project outcomes. Overall, the research provides valuable insights for improving the management and performance of large-scale construction projects.

Legal and Financial Risk Management in Large-Scale Construction Projects

Construction projects involve many parties and experts such as builders, contractors, designers, and consultants, which causes project arrangements to be complicated with many risks and uncertainties, including legal and financial risks. Legal risks include aspects of contracts, permits, certificates, claims, lawsuits, and sanctions. Conflicts or disputes can arise due to legal risks, which can lead to cost overruns and delays in project schedules. The project manager must be able to mitigate these risks in order to achieve the project objectives. This study uses descriptive qualitative and descriptive analytical methods to provide a detailed description of the research problem. The results showed that the risks adversely affect the legal aspects of construction, time, cost, and quality of the project. Legal risks in construction projects can arise due to several reasons, namely problems with documents and contract articles, lawsuits from third parties, and problems in acquiring project land. Good risk management is required to improve project efficiency, and project performance is improved when the organization can create a high fit between risk exposure and risk management profile.

Legal and Financial Risk Management in Large-Scale Construction Projects

Construction projects involve many parties and experts such as builders, contractors, designers, and consultants, which causes project arrangements to be complicated with many risks and uncertainties, including legal and financial risks. Legal risks include aspects of contracts, permits, certificates, claims, lawsuits, and sanctions. Conflicts or disputes can arise due to legal risks, which can lead to cost overruns and delays in project schedules. The project manager must be able to mitigate these risks in order to achieve the project objectives. This study uses descriptive qualitative and descriptive analytical methods to provide a detailed description of the research problem. The results showed that the risks adversely affect the legal aspects of construction, time, cost, and quality of the project. Legal risks in construction projects can arise due to several reasons, namely problems with documents and contract articles, lawsuits from third parties, and problems in acquiring project land. Good risk management is required to improve project efficiency, and project performance is improved when the organization can create a high fit between risk exposure and risk management profile.

Enhancing Open BIM Interoperability: Automated Generation of a Structural Model from an Architectural Model

Building information modelling (BIM) is an appreciated technology in the field of architecture and construction management. Collaboration of information in BIM has not been fully utilized in the structural engineering stream as many engineers keep on working with previous prevailing design approaches. Failure to adequately facilitate automation in design could lead to structural defects, construction rework, or even structural clashes, with major financial implications. Given the inherent complexity of large-scale construction projects, the ‘manual design and detailing’ of structure is a challenging task and prone to human errors. Against this backdrop, this study developed a 4D building information management approach to facilitate automated structural models for professionals designing all the elements required in reinforced concrete (RC) structures like slabs, beams, and columns. The main contribution of this study is to obtain structural models directly from architecture models automatically, which reduces effort and possible errors in the previous prevailing approaches. The framework enables execution of all the model design works automatically through coding. This is achieved by executing a script which is beneficial for integrated project delivery (IPD). The 3D structural model in BIM software presented in this study extracts and transfers the geometrical data and links these data in Industry Foundation Classes (IFC) files using integration facilitated by Python 3.6 and IFCopenshell. The developed automated programme framework offers a cost-effective and accurate methodology to address the limitations and inefficiencies of traditional methods of structural modelling, which had been carried out manually. The authors have developed a novel tool to extract structural models from architectural models without proprietary software, greatly benefiting BIM managers by enhancing 3D BIM models. This advancement toward Open BIM, crucial for the architecture, engineering, and construction (AEC) industry’s future, is accessible to educators and beginners and highlights BIM’s effectiveness in improving structural analysis and productivity. The core finding of this study is to generate a structural model from an architecture model by automating the script with Python integration of IFC and IFCopenshell. The merits of the developed framework are reduced clashes, more economical structural modelling, and fully automated smart work as functions of the IPD.

Optimization and Modelling of the Physical and Mechanical Properties of Grass Fiber Reinforced with Slag-Based Composites Using Response Surface Methodology.

The construction industry's high energy consumption and carbon emissions negatively impact the ecological environment; large-scale construction projects consume much energy and emit a significant amount of CO2 into the atmosphere. Statistics show that 30% of energy loss and 40% of solid waste in the construction industry are generated during construction. Therefore, reducing emissions during construction has significant research potential and value. Many scholars have recently studied eco-friendly building materials to facilitate the use of high-carbon emission materials like cement. Adding fibers to composite materials has become a research hotspot among these studies. Although adding fibers to composite materials has many advantages, it mainly reduces the compressive strength of the composite material. This research used the response surface methodology (RSM) to optimize the raw material ratios and thus improve the performance of plant fiber composite materials. Single-factor experiments were conducted to analyze the effects of grass size, grass content, and quicklime content on the composite materials' compressive strength, flexural strength, and water absorption. The influencing factors and levels for the response surface experiment were determined based on the results of the single-factor analysis. Using the response surface methodology (RSM), a second-order polynomial regression model was established to analyze the interaction effects of the three factors on the composite materials' compressive strength, flexural strength, and water absorption rate. The optimal ratio was determined: the optimized options for grass size, grass content, and quicklime content are 2.0 mm, 8.2 g, and 38 g, respectively. The actual values of compressive strength, flexural strength, and water absorption rate of the composite materials made according to the predicted ratio are 11.425 MPa, 2.145 MPa, and 21.89%, respectively, with a relative error of 8% between the actual and predicted values. X-ray diffraction and scanning electron microscopy were also used to reveal the factors contributing to the relatively high strength of the optimized samples.

Data Processing of Bridge IoT Monitoring Based on Task Scheduling of Cloud Service Listening Signal

For the long-term continuous monitoring of bridge-related indicators, it is necessary to arrange relatively perfect acquisition equipment on the bridge, which can feedback various information parameters of the bridge. However, there are many parameters to feedback the bridge information, which leads to the complex and overstaffed structure of the monitoring system. Furthermore, the huge amount of data collected and the complex calculation process also increase the difficulty of the operation of the monitoring system. In this regard, we should choose more scientific and reasonable indicators, lightweight data structure, stable data transmission, and analysis programs to improve the accuracy of continuous monitoring. To establish a stable and efficient bridge monitoring system, we use the distance coefficient-effective independent algorithm to optimize. Then, we calculate the relevant information of the strain environment with the help of a neural network model, strengthen the training of deep learning through the YOLOv5s model, and improve the task scheduling strategy of attention concentration. Through that, we solve the problem of embedded systems with relatively low computing power. Different weights are assigned to each fused feature map, and the nodes at the highest level and the lowest level are deleted so that a concise and efficient lightweight network model is constructed. Multiple iterations are performed to achieve deeper feature fusion. Therefore, the complexity of the model is effectively reduced, and the monitoring performance can be effectively improved. Finally, through the experimental analysis, it is proved that compared with the traditional fusion model, the number of parameters of the improved fusion network structure in bridge health monitoring is reduced by 7.37%. The detection speed is increased by 18.2%. The amount of computation is reduced by 42.92%, and the average detection accuracy is required to reach 95.33%. It is verified that the proposed method can effectively improve the accuracy and risk control ability of the detection data by learning from the samples with small labels. It also has great practical significance and market value for the design and optimization of the bridge health monitoring system, which is suitable for the monitoring data of large-scale construction projects.

A 3D Parameterized BIM-Modeling Method for Complex Engineering Structures in Building Construction Projects

The structural components of large-scale public construction projects are more complex than those of ordinary residential buildings, with irregular and diverse components, as well as a large number of repetitive structural elements, which increase the difficulty of BIM-modeling operations. Additionally, there is a significant amount of inherent parameter information in the construction process, which puts forward higher requirements for the application and management capabilities of BIM technology. However, the current BIM software still has deficiencies in the parameterization of complex and irregular structural components, fine modeling, and project management information. To address these issues, this paper takes Grasshopper as the core parametric tool and Revit as the carrier of component attribute information. It investigates the parametric modeling logic of Grasshopper and combines the concepts of parameterization, modularization, standardization, and engineering practicality to create a series of parametric programs for complex structural components in building projects. This approach mainly addresses intricate challenges pertaining to the parametric structural shapes (including batch processing) and parametric structural attributes (including the batch processing of diverse attribute parameters), thereby ensuring the efficiency in BIM modeling throughout the design and construction phases of complex building projects.

Multi-objective location-routing model and algorithm study of material reserve bases for large-scale railway engineering construction projects

Material supply is an important support for engineering construction and an important factor affecting the progress and quality of engineering construction. A multi-objective mixed-integer nonlinear programming model is proposed for the location routing problem of the material reserve base of large-scale railway engineering construction projects, considering realistic factors like the construction period, the cycle of the material reserve, the variety of materials, the heterogeneous fleet, and demand splitting. An improved Non-dominated Sorting Genetic Algorithm-II (NSGA- II) is developed for solving this model. This paper optimises obtaining the initial population based on the linear feature of railway engineering, improves the fitness function, and designs a two-stage crossover operator. In addition, the algorithm is compared with the augmented ϵ-constraint method with the help of a practical example. Three metrics are used to assess the performance of the algorithm's outputs, and two metrics are used to assess the similarity of the Pareto fronts obtained by the two algorithms. The results show that the proposed algorithm performs better in terms of ‘quantity’ and ‘spacing’, that the Pareto solutions obtained are accurate, and that the speed of solution is almost 10 times that of the augmented ϵ-constraint (AUGMECON) method.

Optimal design of air distribution systems in large indoor spaces during the construction of walls: A case study on liquefied natural gas terminal storage tanks

Efficiently organizing indoor airflow is crucial for optimizing the working environment in large-scale wall construction projects, particularly when constructing liquefied natural gas membrane tanks. This helps prevent membrane corrosion, optimize construction time, and mitigate future safety risks. However, due to their size, these tanks often experience air stratification, posing challenges in meeting simultaneous temperature, humidity, and uniformity requirements during construction. To effectively address this issue, this study developed Computational Fluid Dynamics models that numerically simulated three commonly used ventilation systems: displacement ventilation system, stratified ventilation system, and fabric duct air supply system. Laboratory experiments were conducted to validate the accuracy of numerical simulation. Additionally, response surface methodology (RSM) was used to determine optimal combinations of air supply parameters for superior environmental uniformity control. The findings indicate that the fabric air supply system is more suitable than displacement and stratified ventilation systems for achieving environmental uniformity control in large spaces. It can limit wall temperature nonuniformity coefficient up to 9 % during summer and provide better uniformity in air velocity during winter conditions. Moreover, under summer conditions, the optimal range for air supply speed is between 9.62 m/s - 11.22 m/s with temperatures ranging from 16 °C to 19 °C, while under winter conditions, the optimal range is between 18.3 m/s −19.3 m/s with temperatures ranging from 33.0 °C to 35.0 °C.The optimal design of the ventilation system is advantageous for various building construction scenarios with stringent temperature and humidity requirements, particularly for large-scale buildings.

A Study on the Enhancement of the Large-Scale Construction Project Capability of Project Management Boards

A Study on the Enhancement of the Large-Scale Construction Project Capability of Project Management Boards

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.

Application of Skip-bin Casting Method in Mass Concrete

With the increasing scale of large-scale civil and industrial construction projects, the problem of controlling temperature cracks is of vital importance in order to ensure the integrity and durability of mass concrete foundation footings. This paper is based on an actual project of construction of a large volume concrete skip bin method, using Midas FEA NX to carry out finite element simulation of the construction process under the skip bin casting of the project, and study the temperature field derived from the simulation, and it is found that the temperature of this large volume concrete project is maximum at about 48h after the concrete casting, and the location is in the center of the concrete.

Dispute Causes Analysis Using Analytical Hierarchy Process for Capstone Projects

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

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success. Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits, minimize unnecessary costs, and provide decision-makers with a robust financial foundation. Additionally, implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages. Developing a practical and feasible fundraising plan, along with stringent fund management practices, can prevent fund wastage and optimize fund utilization efficiency. These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

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

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