Project Cost Prediction Research Articles

Dynamic control method of construction cost based on fuzzy neural network

For improving the dynamic management performance of construction cost, the dynamic control model of cost of construction based on fuzzy neural network is studied. Consider the effect of resource allocation, construction progress and construction quality on the cost of project in construction stage, and build the cost control index for the project in stage of construction. The fuzzy logic model is used in selecting the main cost related indicators from project cost control index system at the construction stage as input neurons of the BP NN, and the BP neural network is employed to output the results of prediction of cost at the construction stage. The project cost prediction results are set as the data basis for the dynamic control of the project cost, determine the most optimistic cost, the most probable cost and the most pessimistic cost of project cost in stage of construction through key chain method, and set buffers to realize dynamic control of project cost in stages of construction. The evaluation results prove that the developed method will be able to accurately predict the project cost in stages of construction, and the cost saved in the construction phase of the project is more than 300000 yuan.

Building Engineering Cost Prediction Based On Deep Learning: Model Construction and Real - Time Optimization

Effective project planning, risk mitigation, and stakeholder satisfaction in the construction business are greatly impacted by accurate cost projection. Overspending, setbacks, and ruined projects are all possible results of imprecise cost estimates. For this reason, it is critical to guarantee the viability and success of a project by increasing the precision of cost predictions. Construction project complexity, a myriad of cost variables, and uncertainty are the obstacles that building engineering cost prediction must overcome. Predictions made using traditional approaches are commonly inaccurate because they fail to fully account for the complex interplay between project factors and expenses. Advanced modelling techniques that can handle complicated data and changeable project contexts are necessary to overcome these obstacles. An approach based on deep learning called Deep CostNet for Building Engineering Technique (DCN-BET) Cost Prediction is presented in this research. Its purpose is to solve the problems associated with building engineering cost prediction. The approach uses deep neural networks to extract intricate patterns from massive amounts of project data collected over time. Improved prediction accuracy and real-time optimisation during project execution are made possible by DCN-BET, which captures the nonlinear correlations between project characteristics and costs. Risk assessment and management, cost forecasting for resource allocation, and project budget estimation and planning are among the few of the many construction industry uses for DCN-BET. The effectiveness of DCN-BET is assessed by conducting thorough simulation analyses in contrast to more conventional cost prediction approaches. Training and testing the model with real-world building engineering datasets allows us to evaluate its accuracy and efficacy in project cost prediction. The results show that DCN-BET has the capacity to support real-time optimisation and significantly improved the accuracy of cost predictions, which improved the overall success and efficiency of the project.

Construction Project Cost Prediction Method Based on Improved BiLSTM

In construction project management, accurate cost forecasting is critical for ensuring informed decision making. In this article, a construction cost prediction method based on an improved bidirectional long- and short-term memory (BiLSTM) network is proposed to address the high interactivity among construction cost data and difficulty in feature extraction. Firstly, the correlation between cost-influencing factors and the unilateral cost is calculated via grey correlation analysis to select the characteristic index. Secondly, a BiLSTM network is used to capture the temporal interactions in the cost data at a deep level, and the hybrid attention mechanism is incorporated to enhance the model’s feature extraction capability to comprehensively capture the interactions among the features in the cost data. Finally, a hyperparameter optimisation method based on the improved particle swarm optimisation algorithm is proposed using the prediction accuracy as the fitness function of the algorithm. The MAE, RMSE, MPE, MAPE, and coefficient of determination of the simulated prediction results of the proposed method on the dataset are 7.487, 8.936, 0.236, 0.393, and 0.996%, respectively, where MPE is a positive coefficient. This avoids the serious consequences of underestimating the cost. Compared with the unimproved BiLSTM, the MAE, RMSE, and MAPE are reduced by 15.271, 18.193, and 0.784%, respectively, which reflects the superiority and effectiveness of the method and can provide technical support for project cost estimation in the construction field.

Deep learning power grid project cost prediction under low-carbon background

Abstract Under the background of low-carbon power system developing towards cleaner, more efficient and more economical direction, accurate prediction of power grid project cost is very important for the construction of power grid project. In order to improve the accuracy of power grid project cost prediction, a power grid project cost prediction method based on deep learning is proposed. First, for the complex and nonlinear features of the line engineering cost data, the XGBoost model is used to analyze the line engineering cost data. Feature selection and data dimension reduction are carried out through the importance ordering of the features analyzed. Then, the engineering cost integration model is established based on the XGBoost model and Stacking. The cost analysis of power grid transmission project shows that the integrated model has better generalization performance and higher prediction accuracy, and can accurately predict the line cost level.

Power grid project cost prediction based on improved CNN under low carbon background

Power grid project cost prediction based on improved CNN under low carbon background

A model for the application of big data analytics in cost prediction and control in construction projects

Abstract Construction project cost control is an important aspect of project management. This paper aims to provide an effective cost control management mode for the construction industry by studying the application of big data technology in cost prediction and control management of construction projects. The standard particle swarm algorithm is optimized by establishing a particle object element model, performing iterative and hierarchical updating of particles, and introducing the Tent mapping strategy. Then it is used in the optimization of the Extreme Learning Machine algorithm to design a construction project cost prediction and control model based on APSO-LLM. The index system of the construction project cost prediction and control model is constructed, and the convergence effect, prediction effect, and case application of the model are analyzed. The APSO-LLM model enhances the convergence speed and search accuracy, boosts the optimal fitness by 0.037, and maintains a construction cost prediction error below 3%. The study proves the APSO-LLM model’s applicability, accuracy, and good prediction effect in cost prediction and construction project control.

Comparison of the Stability and Accuracy of Deterministic Project Cost Prediction Methods in Earned Value Management

Completing a project on time and on budget are essential factors for the success of any project. One technique that allows predicting the final cost of a project is earned value management (EVM). In this technique, different mathematical methods for predicting the final project cost have been proposed over the last 30 years. These formulas make use of activities’ actual costs and durations as the project progresses. EVM is a technique widely used by many project management professionals. However, very few studies have compared the stability and accuracy of the multiple existing methods for predicting the final cost of the project (commonly abbreviated as estimated cost at completion, EAC). This study compares the stability and accuracy of 30 deterministic cost prediction methods (EAC) in EVM. For this purpose, a representative database of 4100 simulated projects of various topological structures is used. Our results suggest that the methods with the simplest mathematical configurations achieve better stability and accuracy performance. Knowing which EVM methods are the most stable and accurate for predicting the final cost of the project will help project practitioners choose the most reliable cost prediction techniques when they are managing their own projects in real contexts.

Design of Engineering Cost Prediction Algorithm Based on Neural Network Model

There are major problems in the current project cost management, which is bound to cause huge economic losses to the construction party, the construction party and even the financial institutions. It can be seen that how to reasonably determine the estimate of the early stage of the project to make scientific investment decisions and how to effectively control the project cost is a very important work. Improving the level of project cost prediction is the premise of reasonably determining the project cost, effectively controlling the construction cost, and realizing the lean project cost management, and is the basis of system planning and decision-making. The data in this paper shows that when the number of nodes is small, the acceleration ratio has little difference, and the increasing trend is basically the same. But after the number of nodes increases to 24, the acceleration ratio curve when the data volume is 2000 and 4000 GB has almost no fluctuation, while the acceleration ratio curve when the data volume is 6000 GB continues to show a small upward trend. Through NN (Neural Network), a model can match all existing engineering data samples, and control the error within a certain range. We think this model has a certain prediction function. However, this matching function is not simple linear regression, because any factor affecting the project cost is nonlinear.

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

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

Hybrid Machine Learning Model Performance in IT Project Cost and Duration Prediction

Hybrid Machine Learning Model Performance in IT Project Cost and Duration Prediction

Building Engineering Cost Prediction Model Based on TSNE and Improved Grey Correlation Algorithm

With the development of digitalization in the construction industry, the complexity of construction projects is increasing. In response to the widely valued cost and quality management of construction projects, improved algorithms based on machine vision learning can effectively cope with the increasingly complex cost prediction of construction projects. In the entire model design, how to complete the selection and processing of key indicators for construction projects is currently a key issue that needs to be urgently solved. This article studied the use of TSNE (T-Distributed Stochastic Neighbor Embedding) and improved grey correlation algorithm to design a cost prediction model, aiming to improve the prediction accuracy of construction project costs and reduce system prediction time. This article analyzed the cost index system and simulation experiments of construction projects, applied machine learning based TSNE and improved grey correlation algorithm, combined index dimensionality reduction processing and construction project unit test set samples, and discussed the following conclusions based on the data results: In the context of intelligent large-scale infrastructure, the construction project cost prediction model using TSNE and improved grey correlation algorithm had higher prediction accuracy than traditional prediction methods in all construction project samples, with an average improvement of 5.1%. At the same time, in terms of model prediction efficiency, the improved algorithm was better than traditional prediction methods, with an average improvement of 12.75%. This indicated that the construction project cost prediction model based on TSNE and improved grey correlation algorithm has a relatively good prediction effect overall.

Research on Intelligent Prediction of Engineering Cost Based on Artificial Intelligence

However, due to the limited engineering information in the investment stage of the project, the estimation of construction cost generally has the disadvantages of large error and long preparation time. Therefore, how to calculate the cost (or investment) of engineering projects accurately, reasonably and quickly is a matter of great concern to engineering project practitioners and researchers. Through the combination of systematic theoretical analysis and practical work experience, the index system for construction project cost prediction is constructed. The artificial intelligence (AI) theory of neural network (NN) is selected, and the basic principle, network model and prediction method of construction project cost prediction with this method are systematically analyzed. As a theoretical model, a new construction project cost prediction model is constructed. This paper mainly studies the application of AI methods in the field of construction project evaluation. Three kinds of AI evaluation models are established by using particle swarm optimization artificial neural network (ANN) and case-based reasoning to predict the construction project evaluation, and their effectiveness is verified by examples.

Research on Construction Project Cost Prediction Model Based on Recurrent Neural Network

At present, the conventional construction project cost prediction method mainly constructs the cost prediction model by quantifying the engineering information, which leads to poor prediction effect due to the lack of construction of cost prediction index system. In this regard, the research of construction cost prediction model based on recurrent neural network is proposed. By classifying and integrating the construction project cost concepts, constructing the prediction index system, combining with the recurrent neural network algorithm, constructing the excitation function and calculating the initialization threshold, and finally constructing the prediction model. In the experiment, the proposed method is verified for the prediction accuracy. After the experiments, it can be proved that when the proposed model is used to predict the engineering cost, the root mean square error of the model output is small and has a more ideal prediction accuracy.

Construction of Unbalanced Engineering Cost Management and Multivariate Statistics System Based on Random Matrix Weight Algorithm

The informatization of engineering cost plays an important role in the cost management and is also one of the important subjects of the informatization in the engineering field. It is the direction of the future project cost management. There is a trend of exponential increase in the information about project cost, which brings a great challenge to cost informatization. On the basis of the construction quantity and cost composition in the process of analyzing the present situation of the project cost, the overall index of the construction quantity and cost is constructed. For bridges and tunnels and to achieve a reasonable index of roadbed, on the basis of considering project at different stages and the demand for different levels of cost management personnel, adopt the method of statistical analysis and neural network, respectively, and set up the project cost fast estimation method and cost prediction model: intelligent prediction model and index system forecasting model. The error analysis and checking calculation of the two models provide the possibility for correctly estimating the engineering quantity and engineering cost in the preliminary design stage. Aiming at the low accuracy of the traditional unbalanced cost data classification algorithm, the dimension of random matrix weight was introduced into the cost data classification. On the basis of analyzing the spatial and temporal characteristics of WAMS measurement data, a high-dimensional random matrix model of WAMS measurement big data was constructed according to the high-dimensional random matrix theory, and a nonequilibrium cost data classification model based on the weighted algorithm of improved fuzzy rules was established. The model is applied to a unit project cost data classification, which verifies the operability of the model and provides a new idea for other units to carry out similar work. Finally, other common problems that should be paid attention to when using multivariate statistical analysis method are put forward.

A Whole Process Cost Prediction System for Construction Projects Based on Improved Support Vector Machines

The current project cost system requires high data scale, small amount of data and large prediction deviation. In order to improve the prediction accuracy of the whole process cost of construction project, this paper designs a whole process project cost prediction system based on improved support vector machine. In the hardware part of the system, the control core adopts arm controller S3C6410 and introduces 4G communication module to analyze the actual engineering data with the support of hardware. In the software part, the whole process cost prediction index system of the construction project is established, the index is reduced by the principal component method, and the support vector machine is improved by particle swarm optimization algorithm to realize the whole process cost prediction of the project. The system function test results show that the average prediction deviation of the designed system is 4.11%, the average prediction deviation of the cost prediction system is 3.05%, and the average prediction deviation of the system is 1.57%.

Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

An Algorithm for Construction Project Cost Forecast Based on Particle Swarm Optimization-Guided BP Neural Network

Construction project cost prediction is an important function in construction-related fields; it can provide an important basis for project feasibility study and design scheme comparison and selection, and its accuracy will directly affect the investment decision of the project. The successful realization of construction cost prediction can bring great convenience to the control and management of construction cost. The purpose of this paper is to study a fast, accurate, convenient, deducible, and rational construction project cost prediction method, to provide a basis for the cost management of the whole life cycle of the project. Therefore, this paper uses particle swarm optimization algorithm to improve BP neural network and proposes a novel construction project cost prediction algorithm based on particle swarm optimization-guided BP neural network. Aiming at the defects of BP neural network updating weights and thresholds with the gradient descent method, this paper uses the advantages of particle swarm optimization in the field of parameter optimization to optimize BP neural network with PSO algorithm. The structure of BP neural network weights and the threshold of each neuron in the coding, through intelligent search for each particle, find the most suitable weights and thresholds, so that the BP neural network has faster convergence speed, better generalization ability, and higher prediction precision. Simulation results also show that the proposed algorithm is competitive enough.

Evaluation of Several Machine Learning Models for Field Canal Improvement Project Cost Prediction

Project cost prediction is one of the key elements in the civil engineering activities development. Project cost is a highly sensitive component to diverse parameters and hence it is associated with complex trends that make it difficult to be predicted and fully understood. Due to the massive advancement of soft computing (SC) and Internet of things (IoT), the main research objective of the current study was initiative. Several machine learning (ML) models including extreme learning machine (ELM), multivariate adaptive regression spline (MARS), and partial least square regression (PLS) were adopted to predict field canal cost. Several essential predictors were used to develop the prediction network “the learning process” including the total length of the PVC pipeline, served area, geographical zone, construction year, and cost and duration of field canal improvement projects (FCIP) construction. Data were collected from the open source published literature. The modeling results evidenced the potential of the applied SC models in predicting the FCIP cost. In numerical magnitude evaluation, MARS model indicated the least value for the root mean square error (RMSE = 27422.7), mean absolute error (MAE = 19761.8), and mean absolute percentage error (MAPE = 0.05454) with Nash–Sutcliffe efficiency (NSE = 0.94), agreement index (MD = 0.89), and coefficient of determination (R2 = 0.94), with best precision of prediction using all predictors, except geographical zone parameter in which less influence on the cost construction is presented. In general, the research outcome gave an informative primary cost initiative for cost civil engineering project.

Research on Substation Project Prediction Method in Power Transmission and Transformation by Improved Neural Network Intelligent Model

On the basis of collecting a large number of practical engineering cases, this paper uses the improved neural network prediction model based on multiple linear regression to build an improved substation project cost prediction model based on the division of substation projects. Using the actual project sample data for empirical research, the results show that the prediction model has high accuracy for the prediction of substation engineering. This research work provides a reliable technical scheme for the cost prediction of power transmission project.

Predict the required cost to develop Software Engineering projects by Using Machine Learning

Software project cost prediction is a very important task during building and developing software projects. This process helps software project engineers to accurately manage and plan their resources in terms of cost estimation. However, Need for accurate cost development prediction model for a software project is not a simple procedure. Predicting the cost required while developing software engineering projects is the most difficult challenge that attracts the attention of researchers and practitioners. This paper adopts a new model in estimating the cost of building or developing software engineering projects using a machine learning approach. The results proves that machine learning methods can be used to predict program cost with high accuracy rate compared with traditional software estimation techniques. The proposed model in this research was trained on the NASA (National Aeronautics and Space Administration) data set, which contains the characteristics of 60 projects in addition to the real cost of the projects. An analysis of the results of the implementation for the proposed methods showed that the cost Predicting process using K-Nearest Neighbours algorithm (KNN), Cascade Neural Networks (CNN) and Elman Neural Networks (ENN) It has the ability to predict the costs required to build or develop software engineering projects, K-Nearest Neighbours algorithm has shown high accuracy for Predict the required cost to develop Software Engineering projects Compared to Cascade Neural Networks and Elman Neural Networks ENN.