CN114782099A - Budget estimation and analysis method for construction cost - Google Patents
Budget estimation and analysis method for construction cost Download PDFInfo
- Publication number
- CN114782099A CN114782099A CN202210447602.6A CN202210447602A CN114782099A CN 114782099 A CN114782099 A CN 114782099A CN 202210447602 A CN202210447602 A CN 202210447602A CN 114782099 A CN114782099 A CN 114782099A
- Authority
- CN
- China
- Prior art keywords
- budget
- project
- value
- vector set
- network model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010276 construction Methods 0.000 title claims abstract description 44
- 238000004458 analytical method Methods 0.000 title claims abstract description 19
- 238000013461 design Methods 0.000 claims abstract description 16
- 238000003062 neural network model Methods 0.000 claims abstract description 14
- 238000012549 training Methods 0.000 claims abstract description 13
- 238000004519 manufacturing process Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 5
- 239000004035 construction material Substances 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 6
- 239000004566 building material Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0206—Price or cost determination based on market factors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Marketing (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Economics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Biophysics (AREA)
- Artificial Intelligence (AREA)
- General Business, Economics & Management (AREA)
- Biomedical Technology (AREA)
- Tourism & Hospitality (AREA)
- Primary Health Care (AREA)
- Human Resources & Organizations (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of construction cost, in particular to a budget estimation and analysis method for construction cost; the invention takes the known engineering design scheme data as a data information source, obtains a first vector set and a second vector set, is used for training the BP neural network model, compares the output budget estimate value with a first threshold value and a second threshold value, detects that the trained BP neural network model can meet the required precision requirement, takes the information of the project to be evaluated as the data information source, outputs the total budget estimate value through the trained BP neural network model, can realize the rapid budget estimate effect, in addition, can also obtain different budget estimate values by adjusting the input information of the BP neural network model, screens out the manufacturing cost key stage and summarizes the budget estimate value by setting the key threshold value, so that managers can visually know and analyze the budget estimate value.
Description
Technical Field
The invention relates to the technical field of construction cost, in particular to a budget estimation and analysis method for construction cost of construction engineering.
Background
With the continuous development of social economy, a lot of waste phenomena exist in the construction process of the current engineering, and the construction quality of the whole engineering project is affected extremely. The project budget is an important link in the project, and refers to the charge standard for carrying out various charges according to corresponding files in the process of executing the construction program. The method has the advantages of strengthening the management of the project budget, reasonably controlling the project cost and ensuring the accuracy of the project cost, and is the primary task of the project budget management.
In the construction of engineering projects, budget work is an important foundation for engineering cost prediction and early control, and if the budget work is not carefully done, conditions of lack of projects, missing projects and inconsistency with construction conditions occur, so that the quality and efficiency of engineering cost control are influenced. However, the existing project cost budget assessment and analysis is that cost workers calculate the project cost according to project design data, the calculation efficiency is low, and calculation errors are easy to occur; meanwhile, the time consumption and accuracy of the engineering cost evaluation are poor, and the use requirement cannot be met.
In summary, the development of a budget estimation and analysis method for construction cost remains a critical problem to be solved urgently in the technical field of construction cost.
Disclosure of Invention
In order to solve the problems, the invention provides a budget estimation and analysis method for construction cost of building engineering, which can output a total budget estimation value through a trained BP neural network model, can realize a quick budget estimation effect, and can obtain different budget estimation values by adjusting input information of the BP neural network model, and screen out a cost key stage and collect the budget estimation values by setting a key threshold value, so that managers can visually know and analyze the budget estimation values.
In order to realize the purpose, the invention provides the following technical scheme:
the invention provides a budget estimation and analysis method for construction cost of building engineering, which comprises the following steps:
(1) acquiring historical engineering design scheme data, extracting an engineering construction list from the historical engineering design scheme, and taking each single item in the engineering construction list as a vector to form a first vector set;
(2) acquiring the cost of the corresponding single item and forming a second vector set;
(3) inputting the first vector set and the second vector set as sample data into a network model, training the network model, outputting a budget estimation value, calculating a ratio of the budget estimation value to a true value, and comparing the budget estimation value with a first threshold value and a second threshold value to finish training the network model;
(4) acquiring design scheme data of a project to be evaluated, extracting a project construction list, acquiring a first vector set of the project to be evaluated, acquiring the construction cost of a corresponding single project, forming a second vector set of the project to be evaluated, inputting the second vector set into a network model, and outputting a total budget estimation value;
(5) adjusting a first vector set of the project to be evaluated and a second vector set of the project to be evaluated, outputting corresponding budget estimates by a network model, obtaining the proportions of the budget estimates of different construction stages of the project to be evaluated in the total budget estimate, analyzing the key construction cost stages of the project to be evaluated, and summarizing the analysis results.
The invention is further configured to: in the step (2), the manufacturing cost of the single item comprises the price of building materials, the price of construction equipment and the artificial price.
The invention is further configured to: in the step (3), the network model is a BP neural network model.
The invention is further configured to: in step (3), the calculation formula of the ratio of the budget estimate to the true value is:
The invention is further configured to: in step (3), if the ratio p of the estimated budget value to the true budget value1：λ1＜p1＜λ2In the formula, λ1Is a first threshold value, λ2If the threshold value is the second threshold value, the network model training is completed, otherwise, the steps are repeatedStep (1) -step (3) to λ1＜p1＜λ2。
The invention is further configured to: in the step (5), the adjusting of the first vector set of the project to be evaluated and the second vector set of the project to be evaluated refers to deleting a specific project at a certain stage in the first vector set of the project to be evaluated and eliminating a corresponding cost.
The invention is further configured to: in step (5), the analysis to obtain the cost key stage of the project to be evaluated is to determine the ratio K of the budget estimate of the stage to the total budget estimate1And setting a key threshold K2Making a comparison if K1＞K2The estimated budget value of the stage is used as the cost-critical stage, otherwise, the estimated budget value is not used as the cost-critical stage.
The invention is further configured to: in the step (5), the summary analysis result is the information of the key construction cost stages in different stages summarized according to a uniform format.
Advantageous effects
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects:
the invention takes the known project design scheme data as a data information source, obtains a first vector set and a second vector set, is used for training the BP neural network model, compares the output budget estimate with a first threshold and a second threshold, detects that the trained BP neural network model can meet the required precision requirement, takes the information of the project to be evaluated as the data information source, outputs the total budget estimate through the trained BP neural network model, can realize the rapid budget estimate effect, can also obtain different budget estimates by adjusting the input information of the BP neural network model, screens out the key construction cost stage and summarizes through setting the key threshold, so that managers can intuitively know and analyze the budget estimate.
Drawings
FIG. 1 is a flow chart of a method for estimating and analyzing budget for construction cost of a building according to the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
Example (b):
as shown in fig. 1, the present invention provides a budget estimation and analysis method for construction cost, comprising the following steps:
(1) obtaining historical engineering design scheme data, extracting an engineering construction list from the historical engineering design scheme, and taking each single item in the engineering construction list as a vector to form a first vector set.
In the embodiment, the historical engineering design scheme data is used as a training data source for the subsequent network model, so that the trained network model can be more real and reliable on one hand, and the trained network model can be conveniently tested on the other hand.
(2) The cost of the corresponding single item is obtained and a second vector set is formed.
Further, the cost of a single item includes the price of building materials, construction equipment and labor.
In this embodiment, the second vector set formed by the above method and the first vector set are used together as input information of the network model, so as to achieve the effect of estimating the engineering budget.
(3) And inputting the first vector set and the second vector set serving as sample data into the network model, training the network model, outputting a budget estimation value, calculating the ratio of the budget estimation value to a true value, and comparing the budget estimation value with a first threshold value and a second threshold value to finish training the network model.
Further, the network model is a BP neural network model.
Further, the calculation formula of the ratio of the budget estimate value to the true value is:
Further, if the ratio p of the estimated budget value to the actual budget value1：λ1＜p1＜λ2In the formula, λ1Is a first threshold value, λ2If the threshold value is the second threshold value, finishing the training of the network model, otherwise, repeating the steps (1) to (3) until the lambda is1＜p1＜λ2。
In this embodiment, the BP neural network model does not need to determine a mathematical equation of a mapping relationship between input and output in advance, only through training itself, learns a certain rule, and obtains a result closest to an expected output value when an input value is given, so that an effect of estimating an engineering budget can be achieved, and the first threshold λ is set1A second threshold lambda2The output estimated engineering budget can be detected, so that the trained network model can meet the precision requirement, in this embodiment, the first threshold λ1A second threshold lambda2Are all positive numbers and less than 1, i.e. 0 < lambda1＜λ2Less than 1, on the basis of ensuring the precision, the estimation of the engineering budget is also ensured not to exceed the actual value, and the exceeding of the budget is avoided.
(4) Acquiring the design scheme data of the project to be evaluated, extracting a project construction list, acquiring a first vector set of the project to be evaluated, acquiring the construction cost of a corresponding single project, forming a second vector set of the project to be evaluated, inputting the second vector set into a network model, and outputting a total budget estimation value.
In the embodiment, the budget estimate of the project to be evaluated can be obtained by means of the trained network model, so that managers can conveniently know the budget condition of the project.
(5) Adjusting a first vector set of the project to be evaluated and a second vector set of the project to be evaluated, outputting corresponding budget estimates by a network model, obtaining the proportions of the budget estimates of different construction stages of the project to be evaluated in the total budget estimate, analyzing the key construction cost stages of the project to be evaluated, and summarizing the analysis results.
Further, adjusting the first vector set of the project to be evaluated and the second vector set of the project to be evaluated means deleting a specific project at a certain stage in the first vector set of the project to be evaluated and eliminating corresponding cost.
Furthermore, the key stage of analyzing and obtaining the cost of the project to be evaluated is to use the ratio K of the budget estimate value of the stage in the total budget estimate value1And setting a key threshold K2Making a comparison if K1＞K2The estimated budget value of the stage is used as the cost-critical stage, otherwise, the estimated budget value is not used as the cost-critical stage.
Furthermore, the summary analysis result is that the information of the key construction cost stages at different stages is summarized according to a uniform format.
In this embodiment, different budget estimates can be obtained by inputting changes of the first vector set and the second vector set, and the budget estimates are compared with the total budget estimate, so as to obtain the budget ratio occupied by the budget estimates2And the key stages of the manufacturing cost can be screened out and summarized, so that managers can visually know the key results of budget valuation analysis.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (8)
1. A budget estimation and analysis method for construction project cost is characterized by comprising the following steps:
(1) acquiring historical engineering design scheme data, extracting an engineering construction list from the historical engineering design scheme, and taking each single item in the engineering construction list as a vector to form a first vector set;
(2) acquiring the cost of the corresponding single item and forming a second vector set;
(3) inputting the first vector set and the second vector set as sample data into a network model, training the network model, outputting a budget estimation value, calculating a ratio of the budget estimation value to a true value, and comparing the budget estimation value with a first threshold value and a second threshold value to finish training the network model;
(4) acquiring design scheme data of a project to be evaluated, extracting a project construction list, acquiring a first vector set of the project to be evaluated, acquiring the manufacturing cost of a corresponding single project, forming a second vector set of the project to be evaluated, inputting the second vector set into a network model, and outputting a total budget estimation value;
(5) adjusting a first vector set of the project to be evaluated and a second vector set of the project to be evaluated, outputting corresponding budget estimates by a network model, obtaining the proportions of the budget estimates of different construction stages of the project to be evaluated in the total budget estimate, analyzing the key construction cost stages of the project to be evaluated, and summarizing the analysis results.
2. The method for estimating and analyzing the budget for manufacturing construction costs according to claim 1, wherein in the step (2), the manufacturing costs of the individual projects include construction material prices, construction equipment prices and labor prices.
3. The method according to claim 1, wherein in step (3), the network model is a BP neural network model.
4. The method for estimating and analyzing budget for construction cost according to claim 1, wherein in the step (3), the calculation formula of the ratio of the estimated budget value to the actual value is:
5. The method according to claim 1, wherein in step (3), if the ratio p between the estimated budget value and the actual value is "p", the estimated budget value is determined to be the actual value1：λ1＜p1＜λ2In the formula, λ1Is a first threshold value, λ2If the threshold value is the second threshold value, finishing the training of the network model, otherwise, repeating the steps (1) to (3) until the lambda is1＜p1＜λ2。
6. The method as claimed in claim 1, wherein in step (5), the adjusting the first vector set of the project to be assessed and the second vector set of the project to be assessed means deleting a specific project at a certain stage in the first vector set of the project to be assessed and rejecting a corresponding cost.
7. The method according to claim 1, wherein in step (5), the analysis for obtaining the cost-critical phase of the project to be assessed is to determine the ratio K of the budget estimate of the phase to the total budget estimate1And setting a key threshold K2Making a comparison if K1＞K2The estimated budget value of the stage is used as the cost-critical stage, otherwise, the estimated budget value is not used as the cost-critical stage.
8. The method for estimating and analyzing budget for construction cost according to claim 1, wherein in step (5), the summarized analysis result is a unified format for summarizing the information of the cost-critical phases of different phases.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210447602.6A CN114782099A (en) | 2022-04-26 | 2022-04-26 | Budget estimation and analysis method for construction cost |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210447602.6A CN114782099A (en) | 2022-04-26 | 2022-04-26 | Budget estimation and analysis method for construction cost |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114782099A true CN114782099A (en) | 2022-07-22 |
Family
ID=82433417
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210447602.6A Pending CN114782099A (en) | 2022-04-26 | 2022-04-26 | Budget estimation and analysis method for construction cost |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114782099A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115994681A (en) * | 2023-03-24 | 2023-04-21 | 江苏苏港智能装备产业创新中心有限公司 | Engineering cost real-time analysis management method and management system thereof |
| CN117764536A (en) * | 2024-01-12 | 2024-03-26 | 四川大学 | Innovative entrepreneur project auxiliary management system based on artificial intelligence |
-
2022
- 2022-04-26 CN CN202210447602.6A patent/CN114782099A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115994681A (en) * | 2023-03-24 | 2023-04-21 | 江苏苏港智能装备产业创新中心有限公司 | Engineering cost real-time analysis management method and management system thereof |
| CN117764536A (en) * | 2024-01-12 | 2024-03-26 | 四川大学 | Innovative entrepreneur project auxiliary management system based on artificial intelligence |
| CN117764536B (en) * | 2024-01-12 | 2024-07-30 | 四川大学 | Innovative entrepreneur project auxiliary management system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zavadskas et al. | Evaluation of ranking accuracy in multi-criteria decisions | |
| León et al. | A fuzzy mathematical programming approach to the assessment of efficiency with DEA models | |
| WO2020228310A1 (en) | Interval and bounded probability mixed uncertainty-based mechanical arm robustness optimization design method | |
| CN108520357B (en) | Method and device for judging line loss abnormality reason and server | |
| CN114782099A (en) | Budget estimation and analysis method for construction cost | |
| CN111104981A (en) | Hydrological prediction precision evaluation method and system based on machine learning | |
| WO2020248228A1 (en) | Computing node load prediction method in a hadoop platform | |
| TW200849345A (en) | Dual-phase virtual metrology method | |
| CN104252652A (en) | Space load predicting method in electricity system | |
| CN112907026A (en) | Comprehensive evaluation method based on editable mesh index system | |
| CN109358608A (en) | A kind of transformer state methods of risk assessment and device based on integrated study | |
| CN117408571A (en) | Full life cycle based construction progress completion data analysis method for building engineering | |
| Ren et al. | Hesitant fuzzy linguistic iterative method for consistency and consensus-driven group decision making | |
| CN115423146A (en) | Self-adaptive runoff forecasting method based on multi-factor nearest neighbor sampling regression and support vector machine | |
| CN107590747A (en) | Power grid asset turnover rate computational methods based on the analysis of comprehensive energy big data | |
| CN111652403A (en) | Feedback correction-based work platform task workload prediction method | |
| Han et al. | Technical comparisons of simulation-based productivity prediction methodologies by means of estimation tools focusing on conventional earthmovings | |
| Li et al. | An improved multiplicative acceptability consistency-driven group decision making with triangular fuzzy reciprocal preference relations | |
| Gulghane et al. | Quantification analysis and prediction model for residential building construction waste using machine learning technique | |
| Wang et al. | Research on construction cost estimation based on artificial intelligence technology | |
| CN106778252B (en) | Intrusion detection method based on rough set theory and WAODE algorithm | |
| Yu et al. | Integrating neurofuzzy system with conceptual cost estimation to discover cost-related knowledge from residential construction projects | |
| Zhang et al. | RSVRs based on feature extraction: a novel method for prediction of construction projects’ costs | |
| CN110855519A (en) | Network flow prediction method | |
| CN110232527A (en) | A kind of evaluation method of Highway Construction Project quality credit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |