Construction Cost Research Articles

Ensemble learning framework for forecasting construction costs

Ensemble learning framework for forecasting construction costs

Conflict of rights in high-rise underground space development: Comprehensive analysis of legal regulation and biomechanical impact

Aiming at the conflict of rights in the development of underground space of high-rise buildings, this paper puts forward a comprehensive analysis framework combining the influence of legal norms and biomechanics. Starting from biomechanics, this paper specifically discusses the rights disputes in three aspects of underground space land transfer fee, the ownership of underground civil air defense project and the ownership of community underground garage, and introduces a biomechanical evaluation model supported by GIS platform to ensure the safety and stability of the project. When considering the underground space land transfer fee, the stability and load-bearing capacity of the soil, which can be analyzed through biomechanical principles, directly affect the feasibility and cost of construction. Just as the skeletal structure of an organism provides the foundation for its movement and survival, the underground geological structure dictates the safety and economic viability of building projects. In the discussion of the ownership of underground civil air defense projects, biomechanical models can be employed to evaluate the potential impact of disasters. By simulating how the structure withstands external forces like earthquakes or explosions, similar to how a creature's body adapts to survive in harsh environments, we can better determine the responsibilities and rights of different parties. Regarding the community underground garage, the layout and design need to conform to human biomechanics. Adequate space for vehicle entry, exit, and pedestrian movement, considering factors such as comfortable walking angles and clear sightlines, ensures convenience and safety, much like how organisms' habitats are optimized for their daily activities. The study found that the municipal government has issued policies to clarify the specific content and scope of underground space use rights, established a unified approval process to strengthen supervision, and promoted the application of biomechanical evaluation models, thereby improving project quality and reducing potential risks. The experimental results show that this comprehensive management strategy effectively alleviates the conflict of rights, promotes the smooth progress of the project, and provides institutional guarantee and technical support for the sustainable development of the city. This paper not only solves the current right conflict problem, but also provides valuable experience for the rational use of urban underground space resources and the maximization of social benefits in the future.

Study on the Macroscopic Properties and Microstructure of High Fly Ash Content Alkali-Activated Fly Ash Slag Concrete Cured at Room Temperature

Fly ash and granulated blast furnace slag are both bulk industrial solid wastes. Using these two raw materials to completely replace cement and prepare alkali-activated fly ash slag concrete (AAFSC) at room temperature can not only efficiently utilize industrial solid waste and reduce the carbon footprint, but also reduce the economic cost and technical difficulty of construction, which is of great significance for promoting the sustainable development of the concrete industry. In this article, the content of fly ash accounted for 80% of the total precursor (fly ash + slag), and a mixed solution of sodium silicate and sodium hydroxide was used as alkali activator to prepare AAFSC by curing at room temperature. The effects of alkali equivalent and activator modulus on compressive strength, impermeability, water absorption, and microstructure were systematically studied and compared with ordinary Portland cement concrete. The conclusions drawn were as follows. The 7-day compressive strength of AAFSC was lower than that of cement concrete, while its 28-day compressive strength was 104.86% to 131.94% of that of cement concrete. AAFSC exhibited excellent impermeability protection performance. The water absorption rate of AAFSC was lower, with A8M1 having a water absorption rate of 2.13%, which was only 60.86% of cement concrete. Through microscopic analysis, it was found that the alkali-activated fly ash slag cementitious matrix had good bonding with the aggregate, and there existed fly ash particles with different degrees of reaction. The Ca/Si value of AAFSC was smaller than that of cement concrete.

Analysis of The Application of Value Engineering In The Construction of Soil Retaining Walls on The Bendung-Bantengan Road Section In Mojokerto District, East Java

Mojokerto, as a key economic center in East Java, faces increasing demands for reliable and efficient road infrastructure to support its economic growth and regional connectivity. The lack of adequate retaining walls along the Bendung-Bantengan road section has resulted in significant road damages, including cracks and subsidence, which pose risks to road users and increase maintenance costs. This study investigates the application of Value Engineering (VE) as a strategic method to enhance the efficiency and cost-effectiveness of retaining wall construction in the area. Using secondary data from topographic surveys and triaxial soil tests, the study evaluates various design alternatives to identify cost savings, stability improvements, and impacts on project duration. The results demonstrate that adopting VE (Alternative 1) reduces construction costs by IDR 284,244,698 through optimized structural dimensions, while maintaining quality and stability standards. Additionally, the project completion period is shortened to 121 days, offering a faster turnaround compared to the initially planned six-month schedule. The findings emphasize VE's potential to optimize material use, minimize waste, and achieve environmental sustainability by reducing carbon emissions during construction. Moreover, VE enables enhanced collaboration among stakeholders, including consultants and contractors, to develop effective and efficient solutions for infrastructure challenges. This research underscores the importance of integrating VE during the planning stages of infrastructure projects, particularly in areas with high erosion risks. By applying VE principles, the Bendung-Bantengan retaining wall project not only meets safety and quality requirements but also contributes to long-term infrastructure resilience and economic efficiency in Mojokerto.

Framework of Smart Multi-purpose Utility Tunnel Information Modeling for Lifecycle Management

Multi-purpose Utility Tunnels (MUTs), as an alternative to buried and above-ground utilities, can accommodate multiple utilities, such as gas, water, and sewer pipes, and electrical and telecommunication cables. Despite MUTs' advantages, their high initial construction cost and the need for coordination among utility companies hindered their wide usage. Building Information Modeling (BIM) can solve some of these problems by facilitating the design, construction, operation, and coordination of utility companies. However, BIM is mainly developed for buildings and extended to some civil structures. There is a lack of a comprehensive framework covering MUT components and information requirements for other use cases, and its integration with Geographic Information Systems (GIS) and other technologies. This paper proposes an integrated framework for Smart MUT Information Modeling (SMUTIM) by (1) gathering the information requirements for all the phases of lifecycle management, (2) analyzing the use cases of SMUTIM, and (3) extending the Industry Foundation Classes (IFC) to SMUTIM. A case study demonstrates the benefits of the proposed framework.

Construction Cost Index: Political, Economic, and Financial Risk Indices Within the European Continent

Construction Cost Index: Political, Economic, and Financial Risk Indices Within the European Continent

Performance of Roller Compacted Concrete Pavement in Arkansas

In 2012, Roller Compacted Concrete (RCC) pavement was placed on a 2-mile section of a deteriorated roadway in Arkansas. Significant distresses in the area had been experienced as a result of the sharp increase in truck traffic associated with recent natural gas exploration efforts. RCC was chosen for the project because it afforded the ability to significantly increase the roadway’s structural capacity while also minimizing construction time and cost. Although some challenges were met during construction, the final product was considered successful, and the pavement has performed well to date. While the surface has not proven to be as aesthetically pleasing as that of a traditional concrete pavement, the pavement has performed admirably and still exhibits good ride quality. After almost three years, IRI values have remained steady in spite of some surface defects. The primary distresses have been surface issues such as popouts and joint deterioration. The pavement has been subjected to relatively heavy traffic and significant seasonal temperature swings, and some cracking has been noted. This paper provides a detailed description of the project and a comprehensive performance summary to date, including a thorough discussion of the existing distresses.

Evaluation of Internally Cured Concrete for Paving Applications

This paper evaluates the use of Internally Cured Concrete (ICC) on Interstates, residential, industrial and other concrete pavement projects from the perspective of design, construction, longevity, curling, durability and life cycle cost. ICC is a concrete mixture in which a portion of the intermediate, and/or fine aggregates (for example, 30 percent of sand) is replaced with similar sized prewetted lightweight aggregate (LWA). Internal curing (IC) is a means to provide hydrating concrete adequate moisture from within the mixture (slab) to replace water lost due to chemical shrinkage. IC may also restore, at least partially, the moisture that escapes through evaporation. IC, which naturally takes place in LWA concrete, has been designed into conventional concrete to release moisture well after placement. ICC has also demonstrated good constructability on field. ICC has shown good durability results in reducing early age shrinkage and associated plastic shrinkage cracking as well as other random cracking on several bridge decks and a few concrete pavement projects. ICC has also demonstrated some benefits in material properties favorably impacting pavement performance that are presented in this paper.

An Effective Single-Station Cooperative Node Localization Technique Using Multipath Spatiotemporal Information

Precise cooperative node localization is essential for the application of multifunctional integrated radio frequency (RF) sensor networks in military and civilian domains. Most geometric localization methods commonly rely on observation data from multiple receiving nodes or anchor points with known positions and synchronized clocks, producing complex system architectures and high construction costs. To address this, our paper proposes an effective single-station cooperative node localization technique, where the observation station only requires two antennas for operation. Leveraging prior knowledge of the geometry of surrounding structures, multiple virtual stations (VSs) are constructed by mining the spatiotemporal information contained in the multipath components (MPCs) to realize target positioning. The proposed method consists of two steps. In the first step, an unambiguous dual-antenna direction-finding algorithm is designed to extract the spatial information of MPCs and construct VSs, allowing a preliminary estimate of the source position (SP). In the second step, the path delays are extracted via matched filtering, while the spatiotemporal information is correlated based on the energy distribution for a more precise SP estimation. Simulations and experimental results demonstrate that our algorithm achieves high-precision single-station localization for a collaborative node, with positioning accuracy typically within 0.1 m.

Electromagnetic Field Modeling and Optimization Design of Axial Magnetic Flux Eddy Current Brake Considering Non‐Ideal Factors

Accurate analytical calculation of electromagnetic field is the basis of research and optimization of axial magnetic flux eddy current brake, and structural optimization design is the key to improving system performance and reducing engineering construction costs. In order to establish an accurate analytical calculation model of electromagnetic field of axial magnetic flux eddy current brake, based on the two‐dimensional analytical calculation model of electromagnetic field, this paper comprehensively considers the influence of non‐ideal factors such as core saturation, transverse dynamic end effect and secondary skin effect on the electromagnetic characteristics of axial magnetic flux eddy current brake, and obtains an improved analytical calculation model of electromagnetic field by introducing corresponding correction coefficients, and verifies the accuracy of the analytical calculation model of electromagnetic field by comparing with the numerical calculation results of three‐dimensional electromagnetic field finite element model. On this basis, an improved multi‐objective particle swarm optimization algorithm is adopted to optimize the system design, so as to increase the electromagnetic braking torque, reduce the electromagnetic axial force and reduce the secondary mass. Finally, the accuracy of the above electromagnetic field analytical calculation model and optimization design results is verified on the experimental research platform of axial magnetic flux eddy current brake, which can provide theoretical basis for subsequent researchers to design and optimize the electromagnetic scheme. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Zoning design of cemented sand and gravel dam materials based on DeepFit–memory gradient search–particle swarm optimization fitting optimization

Cemented sand and gravel (CSG) dams are preferred for their cost-effectiveness and adaptability. This study introduces a zoning fitting optimization design method aimed at improving material strength utilization and reducing construction costs. Using Latin sampling and reduced strength safety calculations, an implicit function relationship is established between zoning parameters and safety through an improved deep neural network (DNN). Subsequent dimensionality reduction facilitates optimization using the memory gradient search–particle swarm optimization (MGS-PSO) algorithm. The method's efficacy is demonstrated by optimizing zoning parameters for Japan's Tobetsu Dam, achieving significant improvements in safety and material use. This approach increases safety by 22% and reduces cementing dosage by 36% compared to traditional methods, underscoring its potential in enhancing dam stability and efficiency.

A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration

ABSTRACT Construction cost prediction remains a complex challenge due to the multidimensional nature of construction data and external factors. The objective of this study is to identify the most effective deep learning model for accurately predicting construction costs by comparing the performance of LSTM, GRU, and Transformer models. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer are advanced machine learning regression models widely utilized for data prediction tasks. This study investigates these models’ performance for construction cost prediction using a multidimensional feature framework. Through comprehensive evaluation and comparison, the Transformer model demonstrated superior performance, particularly excelling in handling complex feature interactions and long-sequence data. The LSTM model, while effective in capturing temporal dependencies, shows reliable performance but lags behind the Transformer in accuracy. The GRU model, although faster in training, proved less accurate and is less effective in handling outliers. Key features such as Total Area (TA), Site Area (SA), and Number of Floors (NF) were identified as significant predictors across all models, with the Transformer model proving particularly adept at capturing complex interactions. By integrating these features, this study contributes to improved cost management, thereby enhancing prediction accuracy and reliability.

Developing the Learning Curve Model to Enhance Construction Project Scheduling and Cost Estimating

Aim The aim of the study is to develop a scheduling and cost estimation model for repetitive construction units by applying the learning curve theory and to contribute to advancements in construction project management practices, promoting efficiency and competitiveness within the industry. Background Construction projects, particularly those with repetitive units like housing developments, face ongoing challenges in accurate scheduling and cost estimation. Traditional estimation methods often overlook the impact of learning effects, which can improve productivity and reduce costs as crews gain experience. Learning curve theory, widely applied in manufacturing, offers a framework to model these gains in construction settings. Integrating learning curves into project planning has the potential to enhance accuracy in forecasting timelines and budgets, ultimately improving project efficiency and resource management. Objective The objective of this study is to develop and apply a learning curve model to enhance scheduling and cost estimation in repetitive construction projects, particularly in a multi-unit housing project. Methods By incorporating historical data and analyzing critical factors that impact project duration and cost, a more reliable forecasting model is developed. The learning curves are created using a three-point approach, supported by artificial neural networks (ANN) and the relative importance index (RII), to systematically assess cost divisions and influential project factors. Results The results indicate that the learning curve model can achieve time savings of 27% and labor cost savings of 36% compared to traditional estimation methods that do not consider the effect of the learning curve in construction projects. Conclusion This research demonstrates that learning curve models, combined with advanced data analysis techniques, provide a robust framework for optimizing project schedules and budgets, ultimately leading to more efficient resource utilization and cost-effective project outcomes. In other words, the study presented in this paper is significant as it can lead to improved project outcomes, cost savings, better resource management, and overall advancement in the construction industry's practices and competitiveness. This approach allows for accurate scheduling and cost forecasting based on data-driven insights.

Assessment of Construction Cost Control Techniques Using BIM Tools

Assessment of Construction Cost Control Techniques Using BIM Tools

Comparison of greenhouse gas emissions associated with the construction of timber, concrete, and steel check dams in Akita, Japan: An input-output analysis.

To mitigate global warming, replacing concrete and steel with timber as the primary construction material for construction projects, such as check dams, is being promoted in Japan and other countries. Timber check dams have more limited installation sites than concrete or steel dams because of installation conditions such as locations less susceptible to debris flows and locations where there is constant running water. However, even when the installation conditions are met, engineers and contractors are reluctant to select timber as a construction material because of its high construction cost. In this study, an input-output table was used to compare the greenhouse gas (GHG) emissions associated with the construction of a timber check dam at the design stage with those associated with the construction of concrete and steel check dams to quantitatively evaluate the added value of timber utilization (in addition to its construction cost). The results revealed that replacing concrete and steel check dams with timber check dams could reduce GHG emissions by 61% and 34%, respectively. This study demonstrated the possibility of evaluating the GHG emissions associated with a construction project at the design stage. Moreover, it highlights the importance of considering the GHG emissions associated with construction materials when selecting the most appropriate materials for public works projects.

Arbitration in Resolving Construction Cost Claim Disputes Due to Time Extensions: A Study of Contract Law in Indonesia

This research aims to analyze the resolution of construction cost claim disputes arising from time extensions, focusing on the arbitration mechanism. The findings indicate that delays in the completion of construction projects not only give rise to cost claim disputes but also necessitate a meticulous analysis of the causes of the delays and the responsibilities of the parties involved. In this regard, arbitration offers a more effective and efficient dispute resolution mechanism than litigation. The advantages of arbitration, including the speed of the process, confidentiality, expertise of arbitrators, procedural flexibility, and binding awards, make it a favourable option for the parties. Therefore, the parties must pay close attention to the arbitration clause in the construction contract. This clause must be formulated clearly and definitively, encompassing the types of construction cost claim disputes arising from time extensions, including claims for price adjustment, overhead costs, extension of performance bonds, material demurrage, and equipment idleness. In addition, the parties can also utilize the Binding Opinions of the BANI Arbitration Center as an instrument for dispute prevention and resolution. For information, Binding Opinions have binding legal force on the parties and are enforceable through the District Court. Thus, the Binding Opinions of the BANI Arbitration Center are beneficial as a dispute resolution instrument and contribute to the development of legal scholarship.

Effect of Cost of Construction of Medium-Level Building Projects on Project Productivity in Kirinyaga County, Kenya

The construction industry is important in society, the economy and the environment. Despite its importance, it is faced with the challenge of the increasing world's urban population at a rate of 200,000 people per day who require affordable housing, social transportation and utility infrastructure. To counter this challenge, the industry will be under a moral obligation to transform by way of reducing construction costs, improving the use of scarce materials and making more eco-efficient over time. Therefore, this study's objective was to evaluate the effect of the cost of construction of medium-level building projects on project productivity in Kirinyaga County, Kenya. The total costs of construction (cost stack) are split into hard costs, soft costs and unforeseen costs. The methodology adopted for data collection was through the administration of structured questionnaires to select seven categories considered to be the main key stakeholders in the construction of building projects. These comprised landlords/homeowners, architects, structural engineers, quantity surveyors, building contractors, construction project managers, and Ministry of Housing and Infrastructure officials. To analyze the results, descriptive statistics and correlational analysis were used. In the analysis, emphasis was placed on the evaluation of the effect of the cost of construction on project completion time, quality and costs. Results from the study show that key factors affecting project productivity through increased costs are delay in payment to contractors and suppliers, high cost of construction materials and choice of construction materials. Other factors are the level of project complexity, site topography, architectural design errors and changes as well as structural design errors and changes.

Design of Artificial Beaches at Sheltered and Exposed Sites

The paper is focused on the design of artificial sand beaches at sheltered and exposed sites. The methodology applied includes the study of the most essential design parameters and the application of numerical models to compute the beach erosion and maintenance. The computed erosion volume decreases for coarser sand (0.5 mm sand instead of 0.3 mm). Beach erosion increases for more graded sand, but the effect is small (10%-15%). The slope of the artificial beach at sheltered sites is commonly between 1 to 15 and 1 to 30 in conditions with a micro tidal range and mild waves. Slopes between 1 to 30 and 1 to 50 are used for more open exposed sites. The effect of the upper and lower beach slope (1 to 15 or 1 to 20) on beach erosion is marginal for sand in the range of 0.3 to 0.5 mm. A break in slope is quickly adjusted by transport processes. The volume of beach sand required may be reduced by constructing a submerged sill at the toe of the beach. Analysis of costs shows that the construction costs including maintenance over a period of 50 years of a submerged sill are about the same as that of beach fill including maintenance. Hence, the beach fill volume can be twice as large for a solution without a sill. Beach erosion due to alongshore transport processes is minimum if the beach line of the planform is perpendicular to the main wave direction (equilibrium beach).

Using mass timber in multi-storey and non-residential construction projects: Motivations and barriers for professionals in Quebec

Despite their environmental benefits and technical viability, mass timber structures adoption remains limited. As an alternative to steel and concrete in non-residential and multi-storey construction, they represent only 10.2% of buildings four or fewer storeys high, 1% of those five or six storeys high and 4% of those seven to twelve storeys high in Quebec. Based on a purposive sample of 42 interviews with various construction industry professionals in Quebec (Canada), the representation of mass timber construction was highlighted. A thematic analysis approach enabled a study of the motivations and barriers to adopting mass timber and the specific reasons behind them, and to determine whether respondents’ perceptions differ significantly depending on their main professional activity. The results corroborate existing literature while offering deeper insights into motivations and barriers, revealing new viewpoints. Respondents cited construction costs, expertise, manufacturing capacity, regulatory limits, and material specifications as the most critical barriers, while environmental impact and aesthetics of wood as key motivators. The response profile analysis suggests that private developers and general contractors should be the primary targets of measures promoting mass timber adoption. This research will aid in refining policies and strategies to encourage the widespread adoption of mass timber in construction practices.

Design of an Algorithm for Rapid Estimation of the Unit Cost of Concrete in Construction Projects

The estimation of the concrete unit cost in construction engineering plays a vital role in controlling construction costs and ensuring timely project completion. To achieve this, a fast estimation algorithm for concrete unit costs is proposed. To ensure accurate estimation and reduce the computational burden on the convolutional neural network (CNN) for rapid estimation, the factors influencing concrete costs are identified and arranged using the interpretive structural modeling (ISM) method. The factors affecting the concrete unit cost of high-rise construction are selected as the input data sequence for the CNN model. The feature map of the input data is extracted through the convolution layer. After applying the pooling operation to the feature map, the processed data is passed into the fully connected layer through the final pooling layer, where the final calculation is performed to obtain the estimated concrete unit cost. Experimental results indicate that to ensure fast convergence and minimize estimation errors in CNN estimation, optimal network parameters are determined through multiple experiments. The best configuration includes a [Formula: see text] convolution kernel and 11 convolutional cores. This algorithm, which uses the ISM method to select influential factors, achieves high accuracy in estimating the concrete unit cost. In actual engineering applications, the error between the estimated and actual results is minimal, with a maximum difference of only 1 yuan.