CN112733227A - Ontology-based bridge automatic design and optimization decision method - Google Patents

Ontology-based bridge automatic design and optimization decision method Download PDF

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CN112733227A
CN112733227A CN202011611538.8A CN202011611538A CN112733227A CN 112733227 A CN112733227 A CN 112733227A CN 202011611538 A CN202011611538 A CN 202011611538A CN 112733227 A CN112733227 A CN 112733227A
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杨刚
宋红红
张田
姜谙男
姜亚丽
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Dalian Maritime University
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Abstract

The invention discloses a body-based bridge automatic design and optimization decision method, which introduces a knowledge base in multiple fields of bridge specification, material implicit carbon energy, cost, optimization, process definition and the like based on body construction, realizes automatic design meeting multiple targets of bridge consideration safety, energy consumption and cost through knowledge reasoning based on semantic network rules, and helps bridge engineers to find an accurate design scheme meeting requirements through an optimization decision model and semantic query, thereby realizing the whole process of bridge multiple-target automatic design and optimization decision.

Description

Ontology-based bridge automatic design and optimization decision method
Technical Field
The invention relates to the technical field of bridge design, in particular to a bridge automatic design and optimization decision method based on a body.
Background
In recent years, people pay more and more attention to the problems of green buildings, zero emission of buildings and the like, and a plurality of researchers are dedicated to reducing energy consumption so as to improve the sustainability of the building industry. The impact of architectural design on environmental impact and cost performance is very large. Studies by scholars in the construction field have demonstrated that the implicit carbon energy of a structure can be reduced by regulating the construction materials in the design stage, but the conventional design method mainly has the following problems: firstly, the influence of each material selection on the safety, environment and cost of the whole bridge structure is difficult to calculate; secondly, the fine quantification of environmental impact and structural cost is difficult to achieve; thirdly, due to the complexity of the bridge structure and the diversity of loads, the safety of the whole structure is recalculated when one material parameter is changed.
At present, the ontology-based method is very effective in realizing multi-objective design of building structures, but the existing ontology-based structural design still has the following defects: firstly, the structural safety constraint is simple, and no design specification is taken as a basis, so the reasoning result cannot be used in practice. Secondly, the number of the obtained structural design schemes is too large, and the most suitable scheme is difficult to select preferentially. Thirdly, the bridge designer has difficulty in designing the bridge by using the body method.
Disclosure of Invention
The invention provides a bridge automatic design and optimization decision method based on a body, which aims to overcome the technical problems.
The invention provides a body-based bridge automatic design and optimization decision method, which comprises the following steps:
constructing an ontology knowledge base according to multi-domain knowledge of bridge design requirements; the multi-domain knowledge of bridge design requirements, comprising: bridge engineering knowledge, bridge design specifications, knowledge of implicit carbon energy and sustainable development fields, knowledge of bridge building material cost and optimization decision-making knowledge;
defining a plurality of classes, a plurality of class hierarchies and a plurality of class attributes in the ontology knowledge base according to the multi-domain knowledge of the bridge design requirement, and creating examples of the classes;
establishing a bridge design semantic web rule based on the ontology knowledge base; the bridge design semantic web rule comprises the following steps: a bridge safety design calculation rule, a bridge carbon-contained energy source calculation rule and a material cost calculation rule;
establishing an optimization decision semantic web rule and a semantic query rule to optimize the calculation result of the bridge design semantic web rule;
inputting bridge design requirements, and operating the bridge design semantic network rule to obtain bridge design parameters; and operating an optimization decision semantic web rule and a semantic query rule to obtain a bridge design optimization decision parameter.
Further, the building of the ontology knowledge base according to the multi-domain knowledge of the bridge design requirements further comprises: listing bridge engineering domain terms according to the multi-domain knowledge of the bridge design requirement; the terms in the field of bridge engineering include: bridge design terms, implied carbon energy terms, bridge building material cost terms, optimization decision terms, and terms in chinese bridge codes.
Further, the plurality of classes includes: a class related to bridge knowledge and a class related to optimization decisions; bridge knowledge-related classes, including: bridge type, bridge structural component, bridge function and bridge material; optimizing decision-related classes, including: optimization method class, optimization problem class and optimization function class.
Further, the bridge design semantic web rule and the optimization decision semantic web rule are both SWRL rules; the semantic query rule is an SQWRL rule.
Further, the bridge safety design calculation rule includes: and the maximum bending moment, the total bending moment, the crack calculation and the deflection calculation rules are caused by the permanent action concentration, the maximum bending moment and the variable action effect.
Further, the bridge implicit carbon energy calculation rule includes: according to the mix proportion of commercial concrete in China, calculating the number of different concrete hidden carbon energy sources through hidden carbon energy sources in an ICE database;
calculating the quantity of the hidden carbon-containing energy sources in the bridge concrete according to the formula (1):
Figure BDA0002873025170000021
in the formula, VIVolume per unit length of bridge, coi-concreteThe amount of carbon energy is implicit to the concrete.
Further, the concrete cost calculation rule includes:
calculating the cost of the bridge concrete according to the formula (2)
Figure BDA0002873025170000022
In the formula, WIIs the weight per unit volume of the bridge structure, CostiRepresenting the cost per square meter of concrete.
Further, establishing an optimization decision semantic web rule and a semantic query rule to optimize the calculation result of the bridge design semantic web rule, including: calculating a calculation result of the bridge design semantic net rule according to an optimization objective function of the formula (3), wherein the constraint of the optimization objective function is mid-span crack and deflection, and the optimization objective is the safety, carbon emission and cost of the bridge;
F(x1,x2,x3)＝A1f(safety)+A2f(carbon emission)+A3f(cost) (3)
In the formula, the variable x1Is the cross-sectional area, variable x2For concrete type, variable x3The type of the steel bar; a. the1、A2、A3All are weight coefficients, which can be adjusted according to the needs of the designer.
Further, after the establishing of the optimization decision semantic web rule and the semantic query rule, the method further includes: verifying the correctness of the logical relation between the knowledge in the ontology knowledge base through a PELLET reasoning machine, and verifying the correctness of the bridge design semantic web rule, the optimization decision semantic web rule and the semantic query rule through an SWRL Tab plug-in.
Further, still include: introducing flow definition knowledge into the ontology knowledge base, adopting BPMN to define a flow model, and converting the flow model into a flow definition class and attributes through ontology mapping; and visualizing the process definition class and the attribute through the OntoGraf Tab visualization plug-in.
The method introduces knowledge in multiple fields such as bridge specification, hidden carbon energy and cost of materials, optimization, process definition and the like based on ontology construction, realizes multi-objective automatic design meeting the requirements of bridge consideration safety, energy consumption and cost through knowledge reasoning based on semantic web rules, and helps bridge engineers to find an accurate design scheme meeting the requirements through an optimization decision model and semantic query, thereby realizing the whole process of multi-objective automatic design and optimization decision of the bridge.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a frame diagram of the present invention;
FIG. 3a is an exemplary diagram of class and attribute creation according to the present invention;
FIG. 3b creates an exemplary diagram for an example of a class of the present invention;
FIG. 4 is a schematic diagram of a specification import system of the present invention;
FIG. 5 is a schematic diagram of an optimization decision development process of the present invention;
FIG. 6 is a diagram of a log of semantic consistency checks performed by the Pellet plugin of the present invention;
FIG. 7 is a log chart of the invention running a SWRL tab plug-in to perform a rule check;
FIG. 8 is a schematic diagram of a case-designed simply supported beam of the present invention;
FIG. 9a is a diagram of the design results of the case of the present invention;
FIG. 9b is a diagram of the result of an optimization decision for the case of the present invention;
FIG. 9c is a crack histogram, a design result for the case of the present invention;
FIG. 9d shows the design results of the case of the present invention-deflection histogram;
FIG. 9e is a design result of the case of the present invention-cost and implicit carbon energy histogram;
FIG. 9f is a histogram of the optimization decision results for the case of the present invention;
FIG. 10a is a diagram of an expanded SWRL rule of the present invention;
FIG. 10b is a graph of the results of a continuous beam reasoning using the present invention;
FIG. 11a is a schematic diagram illustrating a process definition performed by ontology mapping according to the present invention;
FIG. 11b is a schematic diagram of the process definition in the present invention;
FIG. 11c is a flow definition visualization diagram in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 and fig. 2, the present embodiment provides an ontology-based bridge automated design and optimization decision method, including:
101. constructing an ontology knowledge base according to multi-domain knowledge of bridge design requirements; the multi-domain knowledge of bridge design requirements, comprising: bridge engineering knowledge, bridge design specifications, knowledge of implicit carbon energy and sustainable development fields, knowledge of bridge building material cost and optimization decision-making knowledge;
102. defining a plurality of classes, a plurality of class hierarchies and a plurality of class attributes in the ontology knowledge base according to the multi-domain knowledge of the bridge design requirement, and creating examples of the classes;
specifically, steps 101 and 102 are processes of constructing an ontology knowledge base, specifically:
firstly, the method comprises the following steps: the domain and scope of the ontology is determined. The area and the field covered by the knowledge base are mainly divided into six aspects of knowledge, wherein the first aspect is basic knowledge in bridge engineering, and comprises classification of bridges, composition of bridges and the like; the second aspect is bridge specification, including safety design requirements, calculation methods and provisions of relevant parameters; the third aspect is knowledge about the area of implicit carbon energy and sustainable development; four aspects are the cost of materials; a fifth aspect is optimizing knowledge; a sixth aspect is empirical knowledge of the flow definition.
Secondly, the method comprises the following steps: the terms important in the body are enumerated. In enumerating terms, it is important to generate terms related to bridge design, cost, implicit carbon energy, and optimization. In addition to capturing important terms such as bridge length, bridge pier height and the like in bridge engineering, more than one hundred terms specifically listed in Chinese bridge specifications are referred to, such as action, action design value and the like.
Thirdly, the method comprises the following steps: classes and class hierarchies are defined. Building the categories of the collected terms in a top-down manner as shown in fig. 3a, and building the knowledge relationship is an important step in the formation of the system framework.
The class of the ontology knowledge base at least comprises a class related to bridge knowledge, an optimization class and a flow definition class. The bridge knowledge class specifically comprises a bridge type class, a bridge structure component class, a bridge action class and a bridge material class. The bridge type comprises a beam bridge type, a plate bridge type, a cable-stayed bridge type and the like; the bridge component class comprises a beam class, a pier class, an accessory structure class and the like; the bridge action class comprises a permanent action subclass and a variable action subclass; the bridge materials comprise concrete, steel bars and the like
The optimization classes comprise an optimization method class, an optimization problem class and an optimization function class, and the optimization function class comprises an optimization constraint subclass, an optimization target subclass and an optimization variable subclass.
The flow definition class comprises an activity subclass, a task subclass and a gateway subclass.
Fourthly: attributes of the class are defined. The attributes defining a class in the ontology as shown in fig. 3a include relationship attributes representing relationships between different classes and data attributes defining the size of the value. Table 1 shows the middle classification, hierarchy, and attributes of the ontology knowledge base.
TABLE 1
Figure BDA0002873025170000051
Figure BDA0002873025170000061
Fifth, the method comprises the following steps: an instance is created. As shown in fig. 3b, this step will create different bridge sections, concrete types and steel bar types in the body in the form of examples, for example, reinforced concrete material, and the concrete type and steel bar type are selected in the process of creating examples, and the data attributes of the elastic modulus, density, compressive strength, implicit carbon energy and cost are manually input.
103. Establishing a bridge design semantic web rule based on the ontology knowledge base; a bridge design semantic web rule comprising: a bridge safety design calculation rule, a bridge carbon-contained energy source calculation rule and a material cost calculation rule;
specifically, a bridge design specification is introduced into a bridge safety design calculation rule, and the bridge safety design calculation rule mainly comprises three aspects, namely a specification of material characteristics, a specification of parameter selection and a safety requirement specification. The bridge specification is implemented by adding annotations and writing rules in the knowledge base as shown in fig. 4.
The method is specifically realized by writing an SWRL rule through an SWRL Tab, wherein the SWRL rule is in the form of:
B1,…,Bn→A1,…,Am (1)
wherein comma denotes conjunction, A1,…,AmAnd B1,…,BnMay be of the form C (x), P (x, y), sameAs (x, y), or differentFrom (x, y), where C is an OWL description, P is an OWL attribute, and x and y are Datalog variables, OWL instances, or OWL data values.
Table 2 is the SWRL rule for security calculations.
TABLE 2
Figure BDA0002873025170000071
Figure BDA0002873025170000081
On the basis of completing safety design, the invention has the design goal of adding the concrete hidden carbon energy source into consideration. The calculation rule of the concrete implicit carbon energy is described as follows:
the method selects 9 kinds of commercial Chinese concrete with different strengths, each concrete gives a detailed mixing ratio, and most numerical values adopted are obtained from Hammond of university of Pasteur and an ICE carbon and energy list database developed by Jones to calculate the implicit carbon energy of the nine kinds of concrete. The online tool can model up to 3 different concrete mixes and compare their specific carbon footprints. The online tool allows the user to input the composition of the concrete, typically available from a concrete design certificate provided by the concrete supplier.
Factors that are related to the amount of the underlying carbon energy source are the mix proportion of the concrete, the prefabrication or on-site fabrication, the volume of the steel reinforcement and the transport distance. The mix proportion of the concrete can be selected from Chinese commercial concrete mix with the quick look-up manual, the type of the cement is ordinary portland cement, the volume of the reinforcing steel bars is determined according to the reinforcing steel bar distribution amount of the bridge, the concrete is prepared in a prefabricated mode, the distance is 500KM, and the calculation result is shown in Table 3:
TABLE 3
Figure BDA0002873025170000091
The concrete hidden carbon energy is calculated according to the formula (2):
Figure BDA0002873025170000092
in the formula, VIVolume per unit length of bridge, coi-concreteHiding the quantity of carbon energy for concrete;
the specific implementation is to compile an SWRL rule by SWRL Tab, and Table 4 is the SWRL rule implying the calculation of the carbon energy quantity.
TABLE 4
Figure BDA0002873025170000093
The concrete cost calculation process is described as follows:
the calculation process of the concrete cost mainly focuses on the cost difference of materials. Since the concrete prices in different areas of China are different at different times, the calculation is based on the average price of eight concrete suppliers in Beijing area, 10 months and 10 days in 2020, wherein the prices of nine kinds of concrete are shown in Table 5:
TABLE 5
Kind of concrete C25 C30 C35 C40 C45 C50 C55 C60 C70
Cost ([ RMB/m ]3) 447.5 457.5 472.5 487.5 502.5 517.5 532.5 547.5 587.5
The concrete cost is calculated according to equation (3):
Figure BDA0002873025170000101
in the formula, WIIs the weight per unit volume of the bridge structure, CostiRepresenting the cost per square meter of concrete.
The specific implementation is to write SWRL rules through SWRL Tab, and Table 6 is SWRL rules for cost calculation.
TABLE 6
Figure BDA0002873025170000102
104. Establishing an optimization decision semantic web rule and a semantic query rule to optimize the calculation result of the bridge design semantic web rule;
specifically, the flow of optimization decision is as shown in fig. 5, the design results of multiple design schemes generated in 103 are firstly queried, then the three design target results are normalized and calculated through an optimization equation, specifically, the SWRL rule is written through SWRL Tab, and table 7 is the SWRL rule calculated for the optimization decision.
TABLE 7
Figure BDA0002873025170000103
And establishing a semantic web query language, and extracting a bridge design structure and an optimization decision result. The specific implementation is that the SQWRL language is written through the SQWRL Tab, and the SQWRL rule is shown in a table 8.
TABLE 8
Figure BDA0002873025170000104
Figure BDA0002873025170000111
Calculating a calculation result of the bridge design semantic net rule according to an optimization objective function of the formula (4), wherein the constraint of the optimization objective function is mid-span crack and deflection, and the optimization objective is the safety, carbon emission and cost of the bridge;
F(x1,x2,x3)＝A1f(safety)+A2f(carbon emission)+A3f(cost) (4)
In the formula, the variable x1Is the cross-sectional area, variable x2For concrete type, variable x3The type of the steel bar; a. the1、A2、A3All are weight coefficients, which can be adjusted according to the needs of the designer.
And checking the semantic and rule correctness, and specifically operating a Pellet reasoner and an SWRL rule plug-in. Fig. 6 and 7 are inspection completion log diagrams, respectively.
105. Inputting bridge design requirements, and operating a bridge design semantic network rule to obtain multiple bridge design scheme design result parameters; operating an optimization decision semantic web rule and a semantic query rule to obtain a bridge design optimization decision parameter; and operating the query language to extract a design result and an optimization decision result.
Specifically, taking the simple beam design shown in fig. 8 as an example, the specific design requirements are shown in table 9, and design calculation is performed to verify the feasibility of the method. Fig. 9a and 9b are a design result diagram and an optimization decision result diagram, and are described in the form of histograms in fig. 9c to 9f to more objectively and clearly show the results.
TABLE 9
Figure BDA0002873025170000112
In the embodiment, the verification method has expansibility, so that in order to avoid new difficulty for design engineers to use the system to design other bridge types, such as a continuous beam bridge, a relatively complete bridge basic knowledge base is established by the system, and when other bridge calculations are carried out, only rules need to be added through SWRL Tab. In order to verify the convenient expansibility of the system, the design of the continuous beam bridge is expanded, the details are shown in the following table 10, fig. 10a is an expanded SWRL rule, wherein SSB represents a simple beam SWRL rule, CBB represents a continuous beam SWRL rule, and fig. 10b is a continuous beam reasoning result diagram.
Watch 10
Figure BDA0002873025170000121
In this embodiment, a process definition and a process visualization are developed. In order to enable bridge designers to better use the method to design bridges, the invention is additionally provided with a flow definition module. The specific implementation form is a mapping method through the ontology, BPMN flow elements are mapped into the ontology, and then flow visualization is achieved through the OntoGraf Tab. Ontology and BPMN element mapping is shown in fig. 11a, and flow definition and flow visualization are shown in fig. 11b and 11 c.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. An ontology-based bridge automated design and optimization decision method is characterized by comprising the following steps:
constructing an ontology knowledge base according to multi-domain knowledge of bridge design requirements; the multi-domain knowledge of bridge design requirements, comprising: bridge engineering knowledge, bridge design specifications, knowledge of implicit carbon energy and sustainable development fields, knowledge of bridge building material cost and optimization decision-making knowledge;
defining a plurality of classes, a plurality of class hierarchies and a plurality of class attributes in the ontology knowledge base according to the multi-domain knowledge of the bridge design requirement, and creating examples of the classes;
establishing a bridge design semantic web rule based on the ontology knowledge base; the bridge design semantic web rule comprises the following steps: a bridge safety design calculation rule, a bridge carbon-contained energy source calculation rule and a material cost calculation rule;
establishing an optimization decision semantic web rule and a semantic query rule to optimize the calculation result of the bridge design semantic web rule;
inputting bridge design requirements, and operating the bridge design semantic network rule to obtain bridge design parameters; and operating an optimization decision semantic web rule and a semantic query rule to obtain a bridge design optimization decision parameter.
2. The method of claim 1, wherein the building of the ontology knowledge base according to the multi-domain knowledge of the bridge design requirements further comprises:
listing bridge engineering domain terms according to the multi-domain knowledge of the bridge design requirement; the terms in the field of bridge engineering include: bridge design terms, implied carbon energy terms, bridge building material cost terms, optimization decision terms, and terms in chinese bridge codes.
3. The automated bridge design and optimization decision-making method based on ontology of claim 1, wherein the plurality of classes comprise:
a class related to bridge knowledge and a class related to optimization decisions;
bridge knowledge-related classes, including: bridge type, bridge structural component, bridge function and bridge material;
optimizing decision-related classes, including: optimization method class, optimization problem class and optimization function class.
4. The method of claim 1, wherein the bridge design semantic web rules and the optimization decision semantic web rules are both SWRL rules; the semantic query rule is an SQWRL rule.
5. The method of claim 1, wherein the bridge safety design calculation rules comprise:
and the maximum bending moment, the total bending moment, the crack calculation and the deflection calculation rules are caused by the permanent action concentration, the maximum bending moment and the variable action effect.
6. The method of claim 1, wherein the bridge implicit carbon energy calculation rules comprise:
according to the mix proportion of commercial concrete in China, calculating the number of different concrete hidden carbon energy sources through hidden carbon energy sources in an ICE database;
calculating the quantity of the hidden carbon-containing energy sources in the bridge concrete according to the formula (1):
TotalCO2＝∑VI×coi-concrete (1)
In the formula, VIVolume per unit length of bridge, coi-concreteThe amount of carbon energy is implicit to the concrete.
7. The method of claim 6, wherein the bridge cost calculation rules comprise:
Figure FDA0002873025160000021
8. the method of claim 1, wherein the establishing of the optimization decision semantic web rule and the semantic query rule optimizes the calculation result of the bridge design semantic web rule, and the method comprises the following steps:
calculating a calculation result of the bridge design semantic net rule according to an optimization objective function of the formula (3), wherein the constraint of the optimization objective function is mid-span crack and deflection, and the optimization objective is the safety, carbon emission and cost of the bridge;
F(x1,x2,x3)＝A1f(safety)+A2f(carbon emission)+A3f(cost) (3)
In the formula, the variable x1Is the cross-sectional area, variable x2For concrete type, variable x3The type of the steel bar; a. the1、A2、A3All are weight coefficients, which can be adjusted according to the needs of the designer.
9. The method of claim 1, wherein after the establishing of the optimization decision semantic web rule and the semantic query rule, the method further comprises:
verifying the correctness of the logic relation between the knowledge in the ontology knowledge base through a PELLET reasoning machine, and verifying the correctness of the bridge design semantic web rule, the optimization decision semantic web rule and the semantic query rule through an SWRLTab plug-in.
10. The automated bridge design and optimization decision-making method based on ontology of claim 1, further comprising:
introducing flow definition knowledge into the ontology knowledge base, adopting BPMN to define a flow model, and converting the flow model into a flow definition class and attributes through ontology mapping; and visualizing the process definition class and the attribute through the OntoGraf Tab visualization plug-in.
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