CC BY
1
УДК 004.8
5.2.2. Математические, статистические и инструментальные методы в экономике (физико-математические науки, экономические науки)
АВТОМАТИЗИРОВАННЫЙ СИСТЕМНО-КОГНИТИВНЫЙ АНАЛИЗ ВЛИЯНИЯ СОСТАВА БЕТОНА НА ЕГО ФИЗИКО-МЕХАНИЧЕСКИЕ СВОЙСТВАИ СТОИМОСТЬ
Луценко Евгений Вениаминович д.э.н., к.т.н., профессор Web of Science ResearcherID S-8667-2018 Scopus Author ID: 57188763047 РИНЦ SPIN-код: 9523-7101
prof.lutsenko@gmail.com http://lc.kubagro.ru
https://www.researchgate.net/profile/Eugene Lutsenko Кубанский Государственный Аграрный университет имени И.Т.Трубилина, Краснодар, Россия
Бетон является одним из самых древних и самых полуполярных строительных материалов, благодаря своим уникальным физическим свойствам, простоте технологии изготовления и низкой стоимости. Бетон известен человечеству уже как минимум около 6000 лет. За это время накоплен огромный опыт изготовления бетонных смесей различного состава. Строители на протяжении веков пытались добиться нужных им свойств бетона путем добавления в растворы и смеси различных компонент в различных пропорциях. Эта работа является весьма актуальной и интенсивно продолжается и сегодня. Основным методом исследования бетонных смесей на протяжении веков являлся эмпирический экспериментальный метод. Проще говоря, исследователи меняли дозировку различных компонент, добавляли и исключали определенные компоненты и просто на практике изучали физико-механические характеристики бетона, получившегося при использовании некоторой данной рецептуры и технологии. В последние столетия и в наше время появляется и бурно развивается теория бетона и бетонных смесей, которая разрабатывает содержательные модели взаимодействия различных компонент бетона и с помощью этих моделей объясняет получение тех или иных физико- механические свойств бетона на макроуровне. Сегодня настало время, когда к этой работе привлекаются и новые технологии искусственного интеллекта, в частности автоматизированный системно-когнитивный анализ (АСК-анализ) и его программный инструментарий - интеллектуальная система «Эйдос». В данной статье приводится полный численный пример применения АСК-анализа для исследования влияния состава бетона на его
UDC 004.8 UDC 004.8
5.2.2. Mathematical, statistical and instrumental methods of economics (physical and mathematical sciences, economic sciences)
AUTOMATED SYSTEM-COGNITIVE ANALYSIS OF THE INFLUENCE OF CONCRETE COMPOSITION ON ITS PHYSICAL AND MECHANICAL PROPERTIES AND COST
Lutsenko Evgeniy Veniaminovich
Doctor of Economics, Cand.Tech.Sci., Professor
Web of Science ResearcherlD S-8667-2018
Scopus Author ID: 57188763047
RSCI SPIN code: 9523-7101
prof. lutsenko@gmail.com http://lc.kubagro.ru
https://www.researchgate.net/profile/Eugene Lutsenko
Kuban State Agrarian University named after I.T.
Trubilin, Krasnodar, Russia
Concrete is one of the oldest and most semi-polar building materials, due to its unique physical properties, simplicity of manufacturing technology and low cost. Concrete has been known to mankind for at least about 6000 years. During this time, vast experience has been accumulated in the manufacture of concrete mixtures of various compositions. Builders have been trying for centuries to achieve the properties of concrete they need by adding various components to solutions and mixtures in various proportions. This work is very relevant and continues intensively today. The main method of studying concrete mixtures for centuries has been the empirical experimental method. Simply put, the researchers changed the dosage of various components, added and excluded certain components and simply studied in practice the physical and mechanical characteristics of concrete obtained by using some given formulation and technology. In recent centuries and in our time, the theory of concrete and concrete mixtures has been emerging and rapidly developing, which develops meaningful models of the interaction of various components of concrete and with the help of these models explains the obtaining of certain physical and mechanical properties of concrete at the macro level. Today is the time when new artificial intelligence technologies are involved in this work, in particular automated system-cognitive analysis (ASC-analysis) and its software tools - the intelligent system "Eidos". This article provides a complete numerical example of the use of ASC-analysis to study the effect of the composition of concrete on its physical and mechanical properties and cost. This numerical example is hosted in the Eidos cloud and can be installed and studied, as well as improved or adapted and localized by any user of the Eidos system in the world. This allows us to use this article as a basis for laboratory work in disciplines
физико-механические свойства и стоимость. Этот related to artificial intelligence and concrete science
численный пример размещен в Эйдос-облаке и
может быть установлен и изучен, а также
усовершенствован или адаптирован и локализован
любым пользователем системы «Эйдос» в мире.
Это позволяет использовать данную статью в
качестве основы для лабораторной работы по
дисциплинам, связанным с искусственным
интеллектом и бетоноведению
Ключевые слова: АСК-АНАЛИЗ, АВТОМАТИЗИРОВАННЫЙ СИСТЕМНО-КОГНИТИВНЫЙ АНАЛИЗ, ИНТЕЛЛЕКТУАЛЬНАЯ СИСТЕМА «ЭЙДОС», БЕТОН, БЕТОНОВЕДЕНИЕ, ФИЗИКО-МЕХАНИЧЕСКИЕ СВОЙСТВА И СТОИМОСТЬ БЕТОНА
http://dx.doi.org/10.21515/1990-4665-191 -009
Keywords: AS^ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, INTELLIGENT SYSTEM "EIDOS", CONCRETE, CONCRETE SCIENCE, PHYSICAL AND MECHANICAL PROPERTIES AND COST OF CONCRETE
INTRODUCTION
Description of the researched subject area
This issue is discussed in more detail in [1]. Concrete is one of the oldest and most semi-polar building materials due to its unique physical properties, ease of manufacturing technology and low cost. Concrete has been known to mankind for at least 6000 years [1]. During this time, vast experience has been accumulated in the manufacture of concrete mixtures of various compositions. For centuries, builders have tried to achieve the properties of concrete they need by adding various components to solutions and mixtures in various proportions. This work is very relevant and intensively continues today.
The main method for studying concrete mixtures for centuries has been the empirical experimental method. Simply put, the researchers changed the dosage of various components, added and excluded certain components, and simply in practice studied the physical and mechanical characteristics of concrete obtained using a given recipe and technology.
In recent centuries and in our time, the theory of concrete and concrete mixtures (betonology) appears and is rapidly developing, which develops meaningful models of the interaction of various components of concrete and, with the help of these models, explains the obtaining of certain physical and mechanical properties of concrete at the macro level.
This issue is discussed in more detail in [1].
This article provides a complete numerical example of the application of ASC analysis to study the effect of concrete composition on its physical and mechanical properties and cost. This numerical example is hosted in the Eidos cloud and can be installed and studied, as well as improved or adapted and localized by any user of the Eidos system in the world. This allows us to use this
article as a basis for laboratory work in disciplines related to artificial intelligence and concrete science.
Object and subject of research
Object of study (simulation) - concrete.
Subject of study- revealing the dependences of the physical-mechanical and economic properties of concrete on its composition.
Under the economic properties of concrete is understood its cost.
In more detail, the problem solved in the article, its relevance, as well as the purpose of the work are discussed in [1]. 2. METHODS
In more detail, the justification of the requirements for the method of solving the problem, a literary review of the methods of solving problems, their characteristics and assessment of the degree of compliance with reasonable requirements, as well as Automated system-cognitive analysis (ASC-analysis) as a method of solving problems, they are discussed in [1].
The Eidos system is a toolkit for ASC-analysis
Of course, on the Eidos system, as they say, "The light did not converge like a wedge." There are many very worthy artificial intelligence systems. To personally verify this, it is enough to independently search the Internet, just look at the files: NCKR-1 ,NCKR-2,NCKR-3,NCKR-4 or follow the links: https://learn.microsoft.com/ru-ru/dotnet/machine-learning/how-does-mldotnet-work, http://chat.openai.com/, https://poe.com/, https://neural-university.ru/, https://dzen.ru/a/ZCKZRKvrlEMBWOk8, https://ora.ai/,
https://ora.ai/explore?path=trending, https://ora.ai/eugene-lutsenko/aidos,
https://rudalle.ru/, there are still a lot of excellent neural networks: https://problembo.com/en/services (and this might come in handy here - 10 minutes mail: https: //1 Ominutemail .net/).
Useful neural networks and applications for various fields:
^For designers: SiteKick- a neural network for creating landing pages; AdCreative - makes advertising creatives, posters; Looka - logos by text description; Watermark remover - helps to remove watermarks; Booth ai -creates stock photos according to the description; PatternedAI - patterns by text description; Hama - cut out unnecessary elements from a photo or picture; RoomGPT - "trying on" a new renovation for your apartment, helping you choose a design;
□ For photographers:; Pallete fm - colorizes black and white images; Relight - changes chiaroscuro in photos; Photoroom - cut elements from photos, change background; LeiaPix - will turn a 2D photo into 3D.; Nostalgia Photo -improves the quality of old photos; pfpmaker - generator of avatars for social networks; Picsart - replaces or removes unwanted elements in the photo;
For those who edit videos:; CapCut is a handy editor, available in the browser. There is color correction, different effects; vidyo ai - cut video into
short fragments; Reface - change the person's face on the video; Run wayml - a variety of editing tools; ^lou'lab AI - neural network for color correction; Topaz Video AI - greatly improve video quality, remove noise and shaking screen; Luma AI - will make a 3D image from a series of photos; Simplified -picture animation; SpiritMe - your online digital copy;
QFor IT people;; CodePal - writes code from scratch, fixes bugs, evaluates finished code; Codesnippets - creates a code on a text request; Buildt AI - a search engine for VSCode, will find ready-made code on the Internet; Code GPT - code generator plugin for VSCode; Autobackend - automatic backend; Adrenaline - searches for and helps fix bugs in the code; Tabnine -adds code if you fail; ;
H% For schoolchildren and students;; Consensus - database of scientific articles; ExamCram - turns complex learning materials into flashcards and tests for self-examination; MathGPT - solves problems in mathematics; editGPT -fixes bugs in English; Yip - the same, but on the web and with Wikipedia support; ChatBA - makes presentations for you; YouTube Summary with ChatGPT - converts videos or lectures to text; Explain Me Like I'm Five -explains complex scientific terms in simple language;
□ For job seekers:; Interview GPT AI - asks tricky questions and helps prepare for an interview; Resume Worded - improves the summary; kickresume - make a cool resume and write a motivation letter; ^ver Letter AI - write an accompanying text for a resume; ;
" For those who did not help Google:; Chord - will write an abstract in response to a query in a line; Lexii ai - a bot that can link to sources; Perplexity - a neural network-search engine in the form of a browser extension; Nuclia - cloud or server search; Phind - can search for code, will help IT people;
□ For recreation and entertainment:; RadioGPT - radio where music is generated by neurons; EndlessVN - endless visual novel; Natural Language Playlist - will pick up a playlist for 7 hours especially for you; Movie Deep Search - will find a movie on demand; FashionAdvisor AI - tips from a neuro-stylist; Hello History - with the help of it you will communicate with a historical character; Cool Gift Ideas - will choose a gift for a person according to his description; Endel - neuro music that helps you fall asleep; PlaylistAI - will collect a playlist in Apple and Spotify by text or picture.; Tattoos AI - makes sketches for tattoos.
The purpose and objectives of the article are considered in [1].
RESULTS
Task-1. Cognitive structuring of the subject area. Two
interpretations of the classification and descriptive scales and gradations
This issue is discussed in more detail in [1]. In this paper, concrete acts as an object of modeling, its composition as factors (Table 1), and as the results of
these factors, the physical and mechanical properties of concrete and its cost (Table 2):
KOD_OPSC NAME_OPSC
1 BREED OF GROUND STONE
2 COEFF. VOID STONE
3 IST. DENSITY CRUSHED STONE KG/M CUBE
4 BULK DENSITY OF CRUSHED STONE
5 COEFF. G SLIDING GRAINS
6 RASH. CEM. WITHOUT X EXT. KG/M CUBE
7 CONSUMPTION OF CEMENT KG/M CUBE
8 CRUSHED STONE CONSUMPTION KG/M CUBE
9 SAND CONSUMPTION KG/M CUBE
10 WATER CONSUMPTION LITER/M CUBE
11 CHEMICAL. ADDITIVE LITER/MCU
12 WEIGHT BET. MIXTURES KG
13 VOLUME BET. SWEEP M CUBE
14 CHEM. APP. PLASTIF. KG/M CUBE
Source: c:\Aidos-X\AID_DATA\AOOOOOOl\System\Opis_Sc.xlsx
Table2- Classification scales (results of factors)
KOD CLSC NAME CLSC
1 STRENGTH CLASS WHEN COMPRESSED
2 STRENGTH GRADE
3 COMFORTABLE BRAND
4 BRAND FOR FROST.
5 BRAND ON VODONEPR
6 GRADE ACTIVITY OF CEMENT
7 W/C WATER CEMENT REL.
8 WATER PERMEABILITY
9 K1 - COEFF. CHEM. ADDITIVES
10 K2 - COEFF. ACCOUNTING H.D. ON STRENGTH
11 MARK BETH. MIXTURES M KG/SM KV
12 STAND. CEMENT RUB
13 STAND. rubble RUB
14 STAND. SAND RUB
15 STAND. PLASTIF. RUB
16 TOTAL COST OF FSG RUB/M CUBE
Source: c:\Aidos-X\AID_DATA\A0000001\System\Class_Sc.xlsx
3.2. Task-2. Formalization of the subject area
This issue is discussed in more detail in [1]. Source: [25]: https://i.ytimg. com/vi/fLZJImHTALQ/maxresdefault.jpg
Using the standard capabilities of MS Excel, we will present the initial data from Table 3 in the form standard for the Eidos system (Table 4 in [1]). Note: Completely in MS Excel table 4 can be downloaded from the link: http://lc.kubagro.ru/Source_data_applications/Applications-000391/Inp_data.xlsx.
The 2nd figure 8 indicates that in the descriptive scales the total number of gradations is 95, and in table 6 there are only 75 of them. This is because in some descriptive scales there are "Space" gradations or zeros, which, in
accordance with the In Figure 8, it is considered not as significant, but as missing data.
Data imbalance is understood as uneven distribution of the values of the properties of the modeling object or the factors acting on it over the range of changes in the values of numerical scales and between scales, both numerical and textual. The mathematical model of ASC-analysis makes it possible to correctly overcome the imbalance of data by moving from absolute frequencies to relative and quantitative measures of knowledge in system-cognitive models (we will see this below).
Table3- Classification scales and gradations (in full)
KOD CLS NAME CLS
1 STRENGTH CLASS COMPRESSION-B12.5
2 STRENGTH CLASS COMPRESSION-IN-7.5
3 STRENGTH CLASS COMPRESSION-IN 12.5
4 STRENGTH CLASS COMPRESSION-B12.5
5 STRENGTH CLASS COMPRESSION-B15
6 STRENGTH CLASS COMPRESSION-B20
7 STRENGTH CLASS COMPRESSION-B22.5
8 STRENGTH CLASS COMPRESSION-B25
9 STRENGTH CLASS COMPRESSION-B30
10 STRENGTH CLASS COMPRESSION-B35
11 STRENGTH CLASS COMPRESSION-B40
12 STRENGTH CLASS IN COMPRESSION-M 100
13 STRENGTH CLASS COMPRESSION-M100
14 STRENGTH CLASS COMPRESSION-M150
15 STRENGTH CLASS COMPRESSION-M200
16 STRENGTH GRADE-1/5-{100.0000000, 190.0000000}
17 STRENGTH GRADE-2/5-{190.0000000, 280.0000000}
18 STRENGTH GRADE-3/5-{280.0000000, 370.0000000}
19 STRENGTH GRADE-4/5-{370.0000000, 460.0000000}
20 STRENGTH GRADE-5/5-{460.0000000, 550.0000000}
21 BRAND FOR SURFACE-J1
22 STANDARD BRAND-P2
23 STANDARD BRAND-P3
24 STANDARD BRAND-P4
25 BRAND ON DOBOOKL.-P5 b. us.
26 BRAND ON DOBOOKL.-P5 b.us
27 BRAND ON DOBOOKL.-P5 (b us)
28 BRAND ON DOBOOKL.-SZh-2
29 BRAND ON DOBOOKL.-SZh2
30 FROST GRADE-F100
31 FROST GRADE-F150
32 FROST GRADE-F200
33 FROST GRADE-F25
34 FROST GRADE-F50
35 FROST GRADE-F75
36 GRADE ON VODONEPR-W 4
37 GRADE ON VODONEPR-W10
38 GRADE ON VODONEPR-W12
39 GRADE ON VODONEPR-W2
40 GRADE ON VODONEPR-W4
41 GRADE ON VODONEPR-W6
42 GRADE ON VODONEPR-W8
43 BRAND ACTIVITY OF CEMENT-1/5-{400.0000000, 420.0000000}
44 BRAND ACTIVITY OF CEMENT-2/5-{420.0000000, 440.0000000}
45 BRAND ACTIVITY OF CEMENT-3/5-{440.0000000, 460.0000000}
46 BRAND ACTIVITY OF CEMENT-4/5-{460.0000000, 480.0000000}
47 BRAND ACTIVITY OF CEMENT-5/5-{480.0000000, 500.0000000}
48 W/C WATER-CEMENT RATIO-1/5-{0.4496124, 0.6055473}
49 W/C WATER-CEMENT RATIO-2/5-{0.6055473, 0.7614823}
50 W/C WATER-CEMENT RATIO-3/5-{0.7614823, 0.9174172}
51 W/C WATER-CEMENT RATIO-4/5-{0.9174172, 1.0733522}
52 W/C WATER-CEMENT RATIO-5/5-{1.0733522, 1.2292871}
53 WATER PERMEABILITY-1/5-{140.0000000, 160.0000000}
54 WATER PERMEABILITY-2/5-{160.0000000, 180.0000000}
55 WATER PERMEABILITY-3/5-{180.0000000, 200.0000000}
56 WATER PERMEABILITY-4/5-{200.0000000, 220.0000000}
57 WATER PERMEABILITY-5/5-{220.0000000, 240.0000000}
58 K1 - COEFF. CHEM. ADDITIONS-1/5-{0.8000000, 0.8200000}
59 K1 - COEFF. CHEM. ADDITIONS-2/5-{0.8200000, 0.8400000}
60 K1 - COEFF. CHEM. ADDITIONS-3/5-{0.8400000, 0.8600000}
61 K1 - COEFF. CHEM. ADDITIONS-4/5-{0.8600000, 0.8800000}
62 K1 - COEFF. CHEM. ADDITIONS-5/5-{0.8800000, 0.9000000}
63 K2 - COEFF. ACCOUNTING H.D. STRONG-1/5-{1.0000000, 1.0320000}
64 K2 - COEFF. ACCOUNTING H.D. STRONG-2/5-{1.0320000, 1.0640000}
65 K2 - COEFF. ACCOUNTING H.D. STRONG-3/5-{1.0640000, 1.0960000}
66 K2 - COEFF. ACCOUNTING H.D. STRONG-4/5-{1.0960000, 1.1280000}
67 K2 - COEFF. ACCOUNTING H.D. STRONG-5/5-{1.1280000, 1.1600000}
68 MARK BETH. MIXTURES M KG/SM KV-1/5-{100.0000000, 190.0000000}
69 MARK BETH. MIXTURES M KG/SM KV-2/5-{190.0000000, 280.0000000}
70 MARK BETH. MIXTURES M KG/SM KV-3/5-{280.0000000, 370.0000000}
71 MARK BETH. MIXTURES M KG/SM KV-4/5-{370.0000000, 460.0000000}
72 MARK BETH. MIXTURES M KG/SM KV-5/5-{460.0000000, 550.0000000}
73 STAND. CEMENT RUB-1/5-{320.2631661, 547.2705329}
74 STAND. CEMENT RUB-2/5-{547.2705329, 774.2778997}
75 STAND. CEMENT RUB-3/5-{774.2778997, 1001.2852664}
76 STAND. CEMENT RUB-4/5-{1001.2852664, 1228.2926332}
77 STAND. CEMENT RUB-5/5-{1228.2926332, 1455.3000000}
78 STAND. RUBBER-1/5-{722.2309967, 807.1684162}
79 STAND. RUBBER-2/5-{807.1684162, 892.1058357}
80 STAND. RUBBER-3/5-{892.1058357, 977.0432552}
81 STAND. RUBBER-4/5-{977.0432552, 1061.9806747}
82 STAND. RUBBER-5/5-{1061.9806747, 1146.9180942}
83 STAND. SAND RUB-1/5-{183.6644758, 252.0263806}
84 STAND. SAND RUB-2/5-{252.0263806, 320.3882855}
85 STAND. SAND RUB-3/5-{320.3882855, 388.7501903}
86 STAND. SAND RUB-4/5-{388.7501903, 457.1120952}
87 STAND. SAND RUB-5/5-{457.1120952, 525.4740000}
88 STAND. PLASTIF. RUB-1/5-{35.8744514, 62.9995611}
89 STAND. PLASTIF. RUB-2/5-{62.9995611, 90.1246708}
90 STAND. PLASTIF. RUB-3/5-{90.1246708, 117.2497806}
91 STAND. PLASTIF. RUB-4/5-{117.2497806, 144.3748903}
92 STAND. PLASTIF. RUB-5/5-{144.3748903, 171.5000000}
93 TOTAL COST OF FSG RUB/M CUB-1/5-{1107.5740322, 1418.7886437}
94 TOTAL COST OF FSG RUB/M CUB-2/5-{1418.7886437, 1730.0032553}
95 TOTAL COST OF FSG RUB/M CUB-3/5-{1730.0032553, 2041.2178668}
96 TOTAL COST OF FSG RUB/M CUB-4/5-{2041.2178668, 2352.4324784}
97 TOTAL COST OF FSG RUB/M CUB-5/5-{2352.4324784, 2663.6470899}
Source:c:\Aidos-X\AID DATA\A0000001\System\Classes.xlsx
Table4- Descriptive scales and gradations (in full)
KOD ATR NAME ATR
1 ROAD OF CRUSHED STONE-granite
2 BREED OF CRUSHED STONE-KNOWN
3 ROCK OF CRUSHED STONE-limestone
4 ROCK OF CRUSHED STONE-Sand
5 ROAD OF CRUSHED STONE-SMS
6 BREED OF GRINDED STONE-tv.p, gran
7 RING
8 ROAD OF CRUSHED STONE-hard rocks
9 ROAD OF CRUSHED STONE - crushed stone.
10 ROAD OF CRUSHED STONE - crushed stone.
11 COEFF. VOID RUBBER-1/5-{0.4669118, 0.4723974}
12 COEFF. VOID Rubble-2/5-{0.4723974, 0.4778829}
13 COEFF. VOID RUBBER-3/5-{0.4778829, 0.4833685}
14 COEFF. VOID RUBBER-4/5-{0.4833685, 0.4888540}
15 COEFF. VOID RUBBER-5/5-{0.4888540, 0.4943396}
16 IST. DENSITY CRUSHED STONE KG/M KUB-1/5-{2650.0000000, 2664.0000000}
17 IST. DENSITY CRUSHED STONE KG/M KUB-2/5-{2664.0000000, 2678.0000000}
18 IST. DENSITY CRUSHED STONE KG/M KUB-3/5-{2678.0000000, 2692.0000000}
19 IST. DENSITY CRUSHED STONE KG/M KUB-4/5-{2692.0000000, 2706.0000000}
20 IST. DENSITY CRUSHED STONE KG/M KUB-5/5-{2706.0000000, 2720.0000000}
21 BULK DENSITY OF CRUSHED STONE-1/5-{1340.0000000, 1362.0000000}
22 BULK DENSITY OF CRUSHED STONE-2/5-{1362.0000000, 1384.0000000}
23 BULK DENSITY OF CRUSHED STONE-3/5-{1384.0000000, 1406.0000000}
24 BULK DENSITY OF CRUSHED STONE-4/5-{1406.0000000, 1428.0000000}
25 BULK DENSITY OF CRUSHED STONE-5/5-{1428.0000000, 1450.0000000}
26 COEFF. G SLIDING ZEREN-1/5-{1.1000000, 1.1920000}
27 COEFF. G SLIDING ZEREN-2/5-{1.1920000, 1.2840000}
28 COEFF. G SLIDING ZEREN-3/5-{1.2840000, 1.3760000}
29 COEFF. G SLIDING ZEREN-4/5-{1.3760000, 1.4680000}
30 COEFF. G SLIDING ZEREN-5/5-{1.4680000, 1.5600000}
31 RASH. CEM. WITHOUT X EXT. KG/M KUB-1/5-{113.8871473, 197.8683385}
32 RASH. CEM. WITHOUT X EXT. KG/M KUB-2/5-{197.8683385, 281.8495297}
33 RASH. CEM. WITHOUT X EXT. KG/M KUB-3/5-{281.8495297, 365.8307210}
34 RASH. CEM. WITHOUT X EXT. KG/M KUB-4/5-{365.8307210, 449.8119122}
35 RASH. CEM. WITHOUT X EXT. KG/M KUB-5/5-{449.8119122, 533.7931034}
36 CONSUMPTION OF CEMENT KG/M CUB-1/5-{102.4984326, 179.9987461}
37 CONSUMPTION OF CEMENT KG/M CUBE-2/5-{179.9987461, 257.4990596}
38 CONSUMPTION OF CEMENT KG/M CUBE-3/5-{257.4990596, 334.9993730}
39 CONSUMPTION OF CEMENT KG/M CUBE-4/5-{334.9993730, 412.4996865}
40 CONSUMPTION OF CEMENT KG/M CUBE-5/5-{412.4996865, 490.0000000}
41 CRUSHED STONE CONSUMPTION KG/M KUB-1/5-{1017.9897151, 1062.1062633}
42 CRUSHED STONE CONSUMPTION KG/M KUB-2/5-{1062.1062633, 1106.2228115}
43 CRUSHED STONE CONSUMPTION KG/M KUB-3/5-{1106.2228115, 1150.3393596}
44 CRUSHED STONE CONSUMPTION KG/M KUB-4/5-{1150.3393596, 1194.4559078}
45 CRUSHED STONE CONSUMPTION KG/M KUB-5/5-{1194.4559078, 1238.5724560}
46 SAND CONSUMPTION KG/M CUB-1/5-{551.5449724, 756.8359779}
47 SAND CONSUMPTION KG/M CUBE-2/5-{756.8359779, 962.1269834}
48 SAND CONSUMPTION KG/M CUBE-3/5-{962.1269834, 1167.4179890}
49 SAND CONSUMPTION KG/M CUBE-4/5-{1167.4179890, 1372.7089945}
50 SAND CONSUMPTION KG/M CUBE-5/5-{1372.7089945, 1578.0000000}
51 WATER CONSUMPTION LITER/M CUBE-1/5-{126.0000000, 154.8000000}
52 WATER CONSUMPTION LITER/M CUBE-2/5-{154.8000000, 183.6000000}
53 WATER CONSUMPTION LITER/M CUBE-3/5-{183.6000000, 212.4000000}
54 WATER CONSUMPTION LITER/M CUBE-4/5-{212.4000000, 241.2000000}
55 WATER CONSUMPTION LITER/M CUBE-5/5-{241.2000000, 270.0000000}
56 CHEMICAL. ADDITIVE LITER/M CUBE-1/5-{0.9318039, 1.7254431}
57 CHEMICAL. ADDITIVE LITER/M CUBE-2/5-{1.7254431, 2.5190823}
58 CHEMICAL. ADDITIVE LITER/M CUBE-3/5-{2.5190823, 3.3127216}
59 CHEMICAL. ADDITIVE LITER/M CUBE-4/5-{3.3127216, 4.1063608}
60 CHEMICAL. ADDITIVE LITER/M CUBE-5/5-{4.1063608, 4.9000000}
61 WEIGHT BET. MIXTURES KG-1/5-{2068.4800000, 2129.4506545}
62 WEIGHT BET. MIXTURES KG-2/5-{2129.4506545, 2190.4213090}
63 WEIGHT BET. MIXTURES KG-3/5-{2190.4213090, 2251.3919636}
64 WEIGHT BET. MIXTURES KG-4/5-{2251.3919636, 2312.3626181}
65 WEIGHT BET. MIXTURES KG-5/5-{2312.3626181, 2373.3332726}
66 VOLUME BET. Sweep M CUBE-1/5-{0.9700000, 0.9760000}
67 VOLUME BET. Sweep M CUBE-2/5-{0.9760000, 0.9820000}
68 VOLUME BET. Sweep M CUBE-3/5-{0.9820000, 0.9880000}
69 VOLUME BET. Sweep M CUBE-4/5-{0.9880000, 0.9940000}
70 VOLUME BET. Sweep M CUBE-5/5-{0.9940000, 1.0000000}
71 CHEM. APP. PLASTIF. KG/M KUB-1/5-{1.0249843, 1.7999874}
72 CHEM. APP. PLASTIF. KG/M KUB-2/5-{1.7999874, 2.5749906}
73 CHEM. APP. PLASTIF. KG/M KUB-3/5-{2.5749906, 3.3499937}
74 CHEM. APP. PLASTIF. KG/M KUB-4/5-{3.3499937, 4.1249969}
75 CHEM. APP. PLASTIF. KG/M KUB-5/5-{4.1249969, 4.9000000}
Source:c:\Aidos-X\AID DATA\A0000001\System\Attributes.xlsx
Table5- Training set ^ (in full
NAME z: m z z z iß Z z CO Z CTl z 0 z z z 3 z 4 z z 6 z 7 z 8 z 9 z 0 z z z 3 z 4 z z 6 z 7 z 8 z 9 z OEN m N Pi N 3 m N 4 m N ril N
B-7,5 2 16 29 34 39 43 52 53 62 67 68 73 79 84 88 94 3 15 16 21 26 31 36 45 47 51 56 65 66 71
B-7,5 2 16 29 34 39 43 52 53 62 67 68 73 82 84 88 95 6 15 16 21 26 31 36 45 47 51 56 65 66 71
B-7,5 2 16 21 34 39 43 52 54 62 67 68 73 81 84 88 95 6 15 16 21 28 31 36 43 47 51 56 65 66 71
B-7,5 2 16 22 34 39 43 52 56 62 67 68 73 81 84 88 95 3 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 23 34 39 43 52 56 62 67 68 73 78 84 88 94 3 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 24 34 39 43 52 57 62 67 68 73 78 83 89 94 3 15 16 21 28 32 37 43 46 53 56 64 66 72
B-7,5 2 16 22 34 39 43 52 56 62 67 68 73 81 84 88 95 6 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 23 34 39 43 52 56 62 67 68 73 81 84 88 95 6 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 24 34 39 43 52 57 62 67 68 73 81 83 89 95 6 15 16 21 28 32 37 43 46 53 56 64 66 72
B-7,5 2 16 22 34 39 43 52 56 62 67 68 73 80 84 88 94 9 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 23 34 39 43 52 56 62 67 68 73 80 84 88 94 9 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 24 34 39 43 52 57 62 67 68 73 80 83 89 94 9 15 16 21 28 32 37 43 46 53 56 64 66 72
B-7,5 2 16 28 34 39 47 52 53 62 67 68 73 79 84 88 94 3 15 16 21 26 31 36 45 47 51 56 65 66 71
B-7,5 2 16 29 34 39 47 52 53 62 67 68 73 82 84 88 95 6 15 16 21 26 31 36 45 47 51 56 65 66 71
B-7,5 2 16 21 34 39 47 52 54 62 67 68 73 78 84 88 94 3 15 16 21 28 31 36 43 47 51 56 65 66 71
B-7,5 2 16 22 34 39 47 52 56 62 67 68 73 78 84 88 94 3 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 23 34 39 47 52 56 62 67 68 73 78 84 88 94 3 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 24 34 39 47 52 57 62 67 68 73 78 83 88 94 3 15 16 21 28 31 36 43 46 53 56 63 66 71
B-7,5 2 16 22 34 39 47 52 56 62 67 68 73 81 84 88 95 6 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 23 34 39 47 52 56 62 67 68 73 81 84 88 95 6 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 24 34 39 47 52 57 62 67 68 73 81 83 88 95 6 15 16 21 28 31 36 43 46 53 56 63 66 71
B-7,5 2 16 22 34 39 47 52 56 62 67 68 73 80 84 88 94 9 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 23 34 39 47 52 56 62 67 68 73 80 84 88 95 9 15 16 21 28 31 36 43 47 53 56 64 66 71
B-7,5 2 16 24 34 39 47 52 57 62 67 68 73 80 83 88 95 9 15 16 21 28 31 36 43 46 53 56 63 66 71
M 100 12 16 23 33 39 43 75 87 90 93 4 38 50 55 58 61 70 73
M100 13 16 23 33 39 47 75 87 89 94 4 37 50 54 57 61 70 72
M150 14 16 23 34 39 43 76 87 91 95 4 39 50 55 59 62 70 74
M150 14 16 23 34 39 47 76 87 91 95 4 39 50 55 59 62 70 74
M200 15 17 23 34 39 43 77 87 92 96 4 40 50 55 60 63 70 75
M200 15 17 23 34 39 47 77 87 92 96 4 40 50 55 60 63 70 75
B12,5 1 16 28 34 39 43 51 53 62 67 68 73 82 84 88 95 7 15 16 21 26 31 36 45 47 51 56 65 66 71
B12,5 4 16 21 34 39 43 51 54 62 67 68 73 82 84 88 95 7 15 16 21 26 31 36 45 47 51 56 65 66 71
B12,5 4 16 22 34 39 43 51 56 62 67 68 74 78 84 89 94 2 15 16 21 28 32 37 43 47 53 57 64 66 72
B12,5 4 16 23 34 39 43 51 56 62 67 68 74 78 83 89 94 2 15 16 21 28 32 37 43 46 53 57 64 66 72
B12,5 4 16 24 34 39 43 51 57 62 67 68 74 78 83 89 94 2 15 16 21 28 32 37 43 46 53 57 64 66 72
B 12,5 3 16 22 34 39 43 51 56 62 67 68 74 81 84 89 95 7 15 16 21 28 32 37 43 47 53 57 64 66 72
B12,5 4 16 23 34 39 43 51 56 62 67 68 74 81 83 89 95 7 15 16 21 28 32 37 43 46 53 57 64 66 72
B12,5 4 16 24 34 39 43 51 57 62 67 68 74 81 83 89 95 7 15 16 21 28 32 37 43 46 53 57 64 66 72
B12,5 4 16 22 34 39 43 51 56 62 67 68 74 79 84 89 95 9 15 16 21 28 32 37 43 47 53 57 64 66 72
B12,5 4 16 23 34 39 43 51 56 62 67 68 74 79 83 89 95 9 15 16 21 28 32 37 43 46 53 57 64 66 72
B12,5 4 16 24 34 39 43 51 57 62 67 68 74 79 83 89 95 9 15 16 21 28 32 37 43 46 53 57 64 66 72
B12,5 1 16 28 34 39 47 51 53 62 67 68 73 79 84 88 94 2 15 16 21 26 31 36 45 47 51 56 65 66 71
B12,5 1 16 21 34 39 47 51 54 62 67 68 73 79 84 88 94 2 15 16 21 26 31 36 45 47 51 56 65 66 71
В12,5 4 16 22 34 39 47 51 56 62 67 68 74 78 84 88 94 2 15 16 21 28 32 36 43 47 53 56 64 66 71
В12,5 4 16 23 34 39 47 51 56 62 67 68 74 78 84 89 94 2 15 16 21 28 32 37 43 47 53 56 64 66 72
В12,5 4 16 24 34 39 47 51 57 62 67 68 74 78 83 89 94 2 15 16 21 28 32 37 43 46 53 57 64 66 72
В12,5 4 16 22 34 39 47 51 56 62 67 68 74 81 84 88 95 7 15 16 21 28 32 36 43 47 53 56 64 66 71
В12,5 4 16 23 34 39 47 51 56 62 67 68 74 81 84 89 95 7 15 16 21 28 32 37 43 47 53 56 64 66 72
В12,5 4 16 24 34 39 47 51 57 62 67 68 74 81 83 89 95 7 15 16 21 28 32 37 43 46 53 57 64 66 72
В12,5 4 16 22 34 39 47 51 56 62 67 68 74 79 84 88 95 9 15 16 21 28 32 36 43 47 53 56 64 66 71
В12,5 4 16 23 34 39 47 51 56 62 67 68 74 79 84 89 95 9 15 16 21 28 32 37 43 47 53 56 64 66 72
В12,5 4 16 24 34 39 47 51 57 62 67 68 74 79 83 89 95 9 15 16 21 28 32 37 43 46 53 57 64 66 72
В15 5 17 29 34 39 43 50 53 62 67 69 73 82 84 88 95 7 15 16 21 27 31 36 44 47 51 56 65 66 71
В15 5 17 21 34 39 43 50 54 62 67 69 74 82 84 89 95 7 15 16 21 27 32 37 44 47 51 57 65 66 72
В15 5 17 21 34 39 43 50 54 62 67 69 74 78 84 89 94 2 15 16 21 27 32 37 44 47 51 57 65 66 72
В15 5 17 22 34 39 43 50 56 62 67 69 74 81 84 89 95 7 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 23 34 39 43 50 56 62 67 69 74 81 83 89 96 7 15 16 21 29 32 37 42 46 53 57 64 66 72
В15 5 17 24 34 39 43 50 57 62 67 69 74 81 83 90 96 7 15 16 21 29 33 38 42 46 53 57 64 66 73
В15 5 17 22 34 39 43 50 56 62 67 69 74 78 84 89 95 2 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 23 34 39 43 50 56 62 67 69 74 78 83 89 95 2 15 16 21 29 32 37 42 46 53 57 64 66 72
В15 5 17 24 34 39 43 50 57 62 67 69 74 78 83 90 95 2 15 16 21 29 33 38 42 46 53 57 64 66 73
В15 5 17 22 34 39 43 50 56 62 67 69 74 79 84 89 95 9 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 23 34 39 43 50 56 62 67 69 74 79 83 89 95 9 15 16 21 29 32 37 42 46 53 57 64 66 72
В15 5 17 24 34 39 43 50 57 62 67 69 74 79 83 90 95 9 15 16 21 29 33 38 42 46 53 57 64 66 73
В15 5 17 29 34 39 47 50 53 62 67 69 73 82 84 88 95 7 15 16 21 27 31 36 44 47 51 56 65 66 71
В15 5 17 21 34 39 47 50 54 62 67 69 74 82 84 88 95 7 15 16 21 27 31 36 44 47 51 56 65 66 71
В15 5 17 21 34 39 47 50 54 62 67 69 74 78 84 88 94 2 15 16 21 27 31 36 44 47 51 56 65 66 71
В15 5 17 22 34 39 47 50 56 62 67 69 74 81 84 89 95 7 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 23 34 39 47 50 56 62 67 69 74 81 84 89 96 7 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 24 34 39 47 50 57 62 67 69 74 81 83 89 96 7 15 16 21 29 32 37 42 46 53 57 64 66 72
В15 5 17 22 34 40 47 50 56 62 67 69 74 79 84 89 95 9 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 23 34 40 47 50 56 62 67 69 74 79 84 89 95 9 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 24 34 40 47 50 57 62 67 69 74 79 83 89 95 9 15 16 21 29 32 37 42 46 53 57 64 66 72
В15 5 17 22 34 39 47 50 56 62 67 69 74 78 84 89 95 2 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 23 34 39 47 50 56 62 67 69 74 78 84 89 95 2 15 16 21 29 32 37 42 47 53 57 64 66 72
В15 5 17 24 34 39 47 50 57 62 67 69 74 78 83 89 95 2 15 16 21 29 32 37 42 46 53 57 64 66 72
В20 6 17 22 35 39 43 49 56 62 67 69 75 81 84 90 96 7 15 16 21 29 33 38 41 47 53 57 64 66 73
В20 6 17 23 35 39 43 49 56 62 67 69 75 81 83 90 96 7 15 16 21 29 33 38 41 46 53 58 64 66 73
В20 6 17 24 35 39 43 49 57 62 67 69 75 81 83 90 96 7 15 16 21 29 33 38 41 46 53 58 64 66 73
В20 6 17 22 35 39 43 49 56 62 67 69 75 78 84 90 95 2 15 16 21 29 33 38 41 47 53 57 64 66 73
В20 6 17 23 35 39 43 49 56 62 67 69 75 78 83 90 95 2 15 16 21 29 33 38 41 46 53 58 64 66 73
В20 6 17 24 35 39 43 49 57 62 67 69 75 78 83 90 95 2 15 16 21 29 33 38 41 46 53 58 64 66 73
В20 6 17 22 35 39 43 49 56 62 67 69 75 79 84 90 95 9 15 16 21 29 33 38 41 47 53 57 64 66 73
В20 6 17 23 35 39 43 49 56 62 67 69 75 79 83 90 95 9 15 16 21 29 33 38 41 46 53 58 64 66 73
В20 6 17 24 35 39 43 49 57 62 67 69 75 79 83 90 95 9 15 16 21 29 33 38 41 46 53 58 64 66 73
В20 6 17 21 35 40 47 50 54 62 67 69 74 81 84 89 95 7 15 16 21 29 32 37 41 47 51 57 65 66 72
В20 6 17 22 35 39 47 50 56 62 67 69 75 78 84 89 95 2 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 23 35 39 47 50 56 62 67 69 75 78 84 89 95 2 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 24 35 39 47 50 57 62 67 69 75 78 83 90 95 2 15 16 21 29 33 38 41 46 53 57 64 66 73
В20 6 17 22 35 36 47 50 56 62 67 69 75 79 84 89 95 10 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 23 35 36 47 50 56 62 67 69 75 79 84 89 95 10 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 24 35 36 47 50 57 62 67 69 75 79 83 90 96 10 15 16 21 29 33 38 41 46 53 57 64 66 73
В20 6 17 22 35 39 47 50 56 62 67 69 75 81 84 89 96 7 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 23 35 39 47 50 56 62 67 69 75 81 84 89 96 7 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 24 35 39 47 50 57 62 67 69 75 81 83 90 96 7 15 16 21 29 33 38 41 46 53 57 64 66 73
В20 6 17 22 30 36 47 50 56 62 67 69 75 81 84 89 96 1 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 23 30 36 47 50 56 62 67 69 75 81 84 89 96 1 15 16 21 29 32 37 41 47 53 57 64 66 72
В20 6 17 24 30 36 47 50 57 62 67 69 75 81 83 90 96 1 15 16 21 29 33 38 41 46 53 57 64 66 73
В20 6 17 27 35 40 47 50 57 62 67 69 75 81 83 90 96 7 15 16 21 29 33 38 41 46 53 57 63 66 73
В20 6 17 27 35 40 47 49 57 58 63 69 74 81 83 90 96 7 11 20 25 29 33 38 44 46 53 58 65 66 73
В20 6 17 22 35 39 47 49 56 58 63 69 74 81 84 89 95 7 11 20 25 29 33 37 44 47 52 57 65 66 72
В20 6 17 23 35 39 47 49 56 58 63 69 74 81 84 89 95 7 11 20 25 29 33 37 44 46 52 57 65 66 72
В20 6 17 24 35 39 47 49 57 58 63 69 74 81 84 89 95 7 11 20 25 29 33 37 44 46 52 58 65 66 72
В22,5 7 18 21 30 40 43 48 54 62 67 70 75 80 84 89 96 7 15 16 21 30 33 37 41 47 51 57 65 66 72
В22,5 7 18 21 30 41 43 48 54 62 67 70 75 79 84 89 95 10 15 16 21 30 33 37 41 47 51 57 65 66 72
В22,5 7 18 22 30 40 43 48 56 62 67 70 75 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 23 30 40 43 48 56 62 67 70 75 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 24 30 40 43 48 57 62 67 70 76 79 83 91 96 10 15 16 21 30 34 39 41 46 53 58 64 66 74
В22,5 7 18 22 30 41 43 48 56 62 67 70 75 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 23 30 41 43 48 56 62 67 70 75 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 24 30 41 43 48 57 62 67 70 76 80 83 91 96 7 15 16 21 30 34 39 41 46 53 58 64 66 74
В22,5 7 18 26 30 41 43 48 57 62 67 70 76 80 83 91 97 7 15 16 21 30 34 39 41 46 53 58 64 66 74
В22,5 7 18 21 30 41 47 49 54 62 67 70 74 80 84 89 96 7 15 16 21 30 32 37 41 47 51 57 65 66 72
В22,5 7 18 21 30 41 47 49 54 62 67 70 74 84 89 93 5 15 16 21 30 32 37 41 47 51 57 65 66 72
В22,5 7 18 22 30 40 47 49 56 62 67 70 75 79 84 90 96 10 15 16 21 30 33 38 41 47 53 57 64 66 73
В22,5 7 18 23 30 40 47 49 56 62 67 70 75 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 24 30 40 47 49 57 62 67 70 75 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 22 30 41 47 49 56 62 67 70 75 80 84 90 96 7 15 16 21 30 33 38 41 47 53 57 64 66 73
В22,5 7 18 23 30 41 47 49 56 62 67 70 75 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 24 30 41 47 49 57 62 67 70 75 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В22,5 7 18 26 30 41 47 49 57 62 67 70 76 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 21 31 41 47 49 54 62 67 70 74 80 84 89 96 7 15 16 21 30 32 37 41 47 51 57 65 66 72
В25 8 18 21 31 42 47 49 54 62 67 70 74 79 84 89 95 10 15 16 21 30 32 37 41 47 51 57 65 66 72
В25 8 18 22 31 41 47 49 56 62 67 70 75 80 84 90 96 7 15 16 21 30 33 38 41 47 53 58 64 66 73
В25 8 18 23 31 41 47 49 56 62 67 70 75 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 24 31 41 47 49 57 62 67 70 75 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 22 31 42 47 49 56 62 67 70 75 79 84 90 96 10 15 16 21 30 33 38 41 47 53 58 64 66 73
В25 8 18 23 31 42 47 49 56 62 67 70 75 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 24 31 42 47 49 57 62 67 70 75 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 25 31 41 47 49 57 62 67 70 76 80 83 91 96 7 15 16 21 30 34 39 41 46 53 58 64 66 74
В25 8 18 25 31 41 47 49 57 62 67 70 76 79 83 91 96 10 15 16 21 30 34 39 41 46 53 58 64 66 74
В25 8 18 21 31 41 47 49 54 62 67 70 75 80 84 89 96 7 15 16 21 30 32 37 41 47 51 57 65 66 72
В25 8 18 21 31 42 47 49 54 62 67 70 75 79 84 89 95 10 15 16 21 30 32 37 41 47 51 57 65 66 72
В25 8 18 22 31 41 47 49 56 62 67 70 75 80 84 90 96 7 15 16 21 30 33 38 41 47 53 58 64 66 73
В25 8 18 23 31 41 47 49 56 62 67 70 76 80 83 90 96 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 24 31 41 47 49 57 62 67 70 76 80 83 90 97 7 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 22 31 42 47 49 56 62 67 70 75 79 84 90 96 10 15 16 21 30 33 38 41 47 53 58 64 66 73
В25 8 18 23 31 42 47 49 56 62 67 70 76 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 24 31 42 47 49 57 62 67 70 76 79 83 90 96 10 15 16 21 30 33 38 41 46 53 58 64 66 73
В25 8 18 25 31 42 47 49 57 62 67 70 76 80 83 91 97 7 15 16 21 30 34 39 41 46 53 58 64 66 74
В25 8 18 25 31 42 47 49 57 62 67 70 76 79 83 91 96 10 15 16 21 30 34 39 41 46 53 58 64 66 74
В30 9 19 23 32 37 47 48 56 62 67 71 75 80 83 91 96 1 15 16 21 30 34 39 41 46 53 58 64 66 74
В30 9 19 24 32 42 47 48 57 62 67 71 76 80 83 91 96 8 15 16 21 30 34 39 41 46 53 58 64 66 74
В30 9 19 25 32 42 47 48 57 62 67 71 76 80 83 91 97 8 15 16 21 30 34 39 41 46 54 59 64 66 74
В30 9 19 24 32 37 47 48 57 62 67 71 76 80 83 91 96 1 15 16 21 30 34 39 41 46 53 58 64 66 74
В30 9 19 25 32 37 47 48 57 62 67 71 76 80 83 91 97 1 15 16 21 30 34 39 41 46 54 59 64 66 74
В35 10 19 23 32 37 47 48 56 62 67 71 76 80 83 91 97 8 15 16 21 30 34 39 41 46 53 59 64 66 74
В35 10 19 24 32 37 47 48 57 62 67 71 76 80 83 91 97 8 15 16 21 30 34 39 41 46 53 59 64 66 74
В35 10 19 25 32 37 47 48 57 62 67 71 76 80 83 92 97 8 15 16 21 30 35 40 41 46 54 59 64 66 75
В35 10 19 24 32 37 47 48 57 62 67 71 76 80 83 91 97 1 15 16 21 30 34 39 41 46 53 59 64 66 74
В35 10 19 25 32 37 47 48 57 62 67 71 76 80 83 92 97 1 15 16 21 30 35 40 41 46 54 59 64 66 75
В40 11 20 23 32 38 47 48 56 62 67 72 76 80 83 92 97 7 15 16 21 30 35 40 41 46 53 59 64 66 75
В40 11 20 24 32 38 47 48 57 62 67 72 77 80 83 92 97 7 15 16 21 30 35 40 41 46 53 59 64 66 75
В40 11 20 25 32 38 47 48 57 62 67 72 77 80 83 92 97 7 15 16 21 30 35 40 41 46 54 60 64 66 75
Source: c:\Aic
os-X\AID_DATA\A0000001\System\EventsKO.xlsx
Note that the Eidos system usually uses databases with the dbf extension. They open in MS Excel or can be converted to xlsx files using online services or in 5.12 mode (this mode of the Eidos system is written in Python).
Task-3. Synthesis of statistical and system-cognitive models. Multiparameter typing and partial knowledge criteria
This issue is discussed in more detail in [1].
Table6- Absolute frequency matrix (ABS statistical model)
Classes Sum
1 ... j ... W
Factor values 1 N11 N1 / N iy1W
...
i Ni N N iV iW W NS = INi/ j=1
...
M N N N iyMW
Total number of features by class M NS = IN/ Z=1 W M NSS = I IN i=1 J =1
The total number of training sample objects by class NSj W Nss= I NSj j=1
This issue is discussed in more detail in [1].
Table7 - Matrix of conditional and unconditional percentage distributions _(statistical models PRC1 and PRC2)_
Classes Unconditional Feature Probability
1 ... j ... W
Factor values 1 Pi pu P 1 1W
...
i Pi p II p 1 iW P _ NS Ps N SS
...
M P 1 Ml P rMj P 1 MW
Unconditional class probability Pj
This issue is discussed in more detail in [1].
Let us pay special attention to the fact that the comparison of actual and theoretical absolute frequencies by division leads to zero when normalized (which is necessary for the application of additive integral criteria) by taking the logarithm and subtracting 1 to the same models as the comparison of conditional and unconditional relative frequencies by dividing with the same normalization methods. Thus, if, based on the absolute frequency matrix, we calculate the matrices of conditional and unconditional percentage distributions, and then compare the actual absolute frequencies with the theoretical ones by subtracting and dividing, and also compare the conditional and unconditional relative frequencies also by subtracting and dividing, and normalize to 0 by taking logarithm and by subtracting 1, then 3 statistical models are obtained: an absolute frequency matrix and two relative frequency matrices, those. conditional and unconditional percentage distributions, as well as a total of 7 system-cognitive models. There are simply no other system-cognitive models calculated on the basis of the above statistical models. This is the configurator of statistical and cognitive models in the sense of V.A. Lefevre. Under the configurator, V.A. Lefevre understood the minimum complete set of conceptual scales or constructs, i.e. concepts sufficient to adequately describe the subject area [1]1. It should be noted that all these models are calculated in the Eidos intellectual system.
This issue is discussed in more detail in [1].
1 See 1.2.1.2.1.1. Definition of the concept of a configurator, http://lc.kubagro.ru/aidos/aidos06 lec/index.htm http://ej.kubagro.ru/2023/07/pdf/09.pdf
Table8- Various analytical forms of partial knowledge criteria used _in ASC-analysis and the Eidos system_
Name of the knowledge model and particular criterion Expression for a particular criterion
Through relative frequencies Through absolute frequencies
ABS, the matrix of absolute frequencies, Nij - the actual number of occurrences of the i-th attribute in objects of the j-th class; N-- -' ' the theoretical number of occurrences of the i-th feature in objects of the j-th class; Ni is the total number of features in the i-th line; Nj is the total number of features or objects of the training sample in the j-th class; N is the total number of features in the entire sample (Table 7) Nij - фактическая частота; W M W M n = z n; n = z n;n = zz n; j=1 i=1 i=1 j =1 - nin, Nj = ——--теоретическая частота.
PRC1, the matrix of conditional Pij and unconditional Pi percentage distributions, Nj is the total number of features by class --- P = N . P = N
PRC2, the matrix of conditional Pij and unconditional Pi percentage distributions, Nj is the total number of training sample objects by class ij N/ i N
INF1, partial criterion: the amount of knowledge according to A. Kharkevich, 1st option for calculating probabilities: Nj - the total number of features for the j-th class. The probability that if an object of the j-th class has a feature, then this is the i-th feature Ij = Yx Log 2 P i N„ NN I = Y X Log 11 = Y X Log j
INF2, partial criterion: the amount of knowledge according to A. Kharkevich, 2nd option for calculating probabilities: Nj - the total number of objects in the j-th class. The probability that if an object of the j-th class is presented, then the i-th attribute will be found in it. h YxLOg2 N YxLOg2 NN V i j
INF3, partial test: Chi-square: differences between actual and theoretically expected absolute frequencies --- - NN, I = N.. N. = N. i 1 j j j j n
INF4, partial criterion: ROI - Return On Investment, 1st option for calculating probabilities: Nj - the total number of features for the j-th class P P - P I = j 1 = j i N„ NvN I = ij 1 = j 1
INF5, partial criterion: ROI - Return On Investment, 2nd option for calculating probabilities: Nj - the total number of objects in the j-th class j P P i ij — i i j Nj NlNJ
INF6, partial criterion: difference between conditional and unconditional probabilities, 1st option for calculating probabilities: Nj - total number of features in the j-th class I = P P ij ij i I = nV N = W-Wj
INF7, particular criterion: the difference between the conditional and unconditional probabilities, 2nd option for calculating probabilities: Nj - the total number of objects in the j-th class iJ Nv N NvN
Table designations: This issue is discussed in more detail in [1].
Table9- Matrix of the system-cognitive model
Classes Significance of the factor
1 ... J ... W
Factor values 1 In I1j i1W W2 w-1 У(I1 j -1)
...
i I,1 1W 1 W 2 1 у (i ■■ -1 ) w -1 j-t"
...
M 1M1 IMj 1 MW 1 W 2 W-1У(Imj Im)
Class reduction degree sS1 s 1 W M 2 H : 2 1 ту (i, I) 2 (W ■ IM -1)у у 1
This issue is discussed in more detail in [1].
Table10- Configurator of system-cognitive models
of ASC-analysis and intellectual system "Eid os"
Comparison method Normalization is not required Normalization to 0 by taking the logarithm Normalization to 0 by subtracting 1
Comparison of actual and theoretical absolute frequencies By division --- INF1, INF2, Alexander Kharkevich INF4, INF5, ROI
By subtraction INF3, Karl Pearson's %-square --- ---
Comparison of conditional and unconditional relative frequencies By division --- INF1, INF2, Alexander Kharkevich INF4, INF5, ROI
By subtraction INF6, INF7 --- ---
This issue is discussed in more detail in [1].
It is significant that Karl Pearson's x-squared measure model from statistics turned out to be mathematically closely related to the return on investment (ROI) used in economics in the theory of investment portfolio management and with Alexander Kharkevich's measure of information from semantic information theory and knowledge management theory. All these models are calculated in the Eidos intellectual system.
This issue is discussed in more detail in [1].
The solution of task-4: assessment of the reliability of the model, task-5: selection of the most reliable model, task-6: identification and forecasting, is given in [1]. In the same work, the integral criteria "Volume of knowledge" and "Semantic resonance of knowledge" are considered, as well as some important mathematical properties of integral criteria.
Tablell- Clarification of the terminology of ASC analysis
No. Traditional terms (synonyms) New term Formula
1 1. Significance of the value of the factor (attribute). 2. Differentiating power of the value of the factor (attribute). 3. The value of the factor (attribute) value for solving the problem of identification and other problems The root of the information power of the factor value 1 W 2 1 E {7 -7 ) w -1E
2 1. The degree of determinism of the class. 2. The degree of conditionality of the class. Root of class information power 2 1 M 2 m-1e-7')
3 1. The quality of the model. 2. The value of the model. 3. The degree of formation of the model. 4. Quantitative measure of the degree of severity of regularities in the modeled subject area The root of the information power of the model я=2 1 WM 2 1 eeI, 7) (W • m -1) £ try ;
Solving the problem of identification and forecasting in the Eidos system
In ASC-analysis, advanced forecasting methods based on the scenario method of ASC-analysis or scenario ASC-analysis have been developed and implemented in the Eidos system. But the tasks of this work do not include their detailed consideration, especially since they are consecrated in detail both at the theoretical level and with detailed numerical examples in [4-7] and in a number of others.
Of these output forms, consider only two: 4.1.3.1 and 4.1.3.2 (Figure 18 in [1]). These output forms, taking into account what has been said above about the integral criteria of the "Eidos" system, are said to be "intuitive" and do not require special comments.
Task-7. Decision Support
The solution of task-7 is given in [1] and includes the following subtasks:
- simplified decision-making as an inverse forecasting problem, positive and negative information portraits of classes, SWOT analysis;
- a developed decision-making algorithm in adaptive intelligent control systems based on ASC-analysis and the Eidos system.
Task-8. Examining the object of modeling by examining its model
The solution of task-8 is given in [1] and includes the following subtasks:
- inverted SWOT Diagrams of Descriptive Scale Values (Semantic Potentials);
- cluster-constructive analysis of classes;
- cluster-constructive analysis of the values of descriptive scales;
- knowledge Model of the Eidos system and Nonlocal Neurons;
- non-local neural network;
- 3D Integral Cognitive Maps;
- 2D Integral Cognitive Maps of Meaningful Class Comparison (Mediated Fuzzy Plausible Reasoning);
- 2D-integrated cognitive maps of meaningful comparison of factor values (mediated fuzzy plausible reasoning);
- cognitive functions;
- significance of descriptive scales and their gradations.
Table12- The strength of the influence of factor values on the behavior of the simulation _object in the INF4 SC-model___
No. No.% Code Factor value name Significance, % Significance cumulatively, %
1 1.333 61 WEIGHT BET. MIXTURES KG-1/5-{2068.4800000, 2129.4506545} 14,590 14,590
2 2.667 62 WEIGHT BET. MIXTURES KG-2/5-{2129.4506545, 2190.4213090} 8.674 23.264
3 4,000 4 ROCK OF CRUSHED STONE-Sand 6.576 29.840
4 5.333 50 SAND CONSUMPTION KG/M CUBE-5/5-{1372.7089945, 1578.0000000} 6.576 36.416
5 6.667 70 VOLUME BET. Sweep M CUBE-5/5-{0.9940000, 1.0000000} 6.576 42.992
6 8,000 55 WATER CONSUMPTION LITER/M CUBE-5/5-{241.2000000, 270.0000000} 6.524 49.516
7 9.333 60 CHEMICAL. ADDITIVE LITER/M CUBE-5/5-{4.1063608, 4.9000000} 5.659 55.175
8 10.667 5 ROAD OF CRUSHED STONE-SMS 4.019 59.194
9 12,000 40 CONSUMPTION OF CEMENT KG/M CUBE-5/5-{412.4996865, 490.0000000} 2.865 62.059
10 13.333 75 CHEM. APP. PLASTIF. KG/M KUB-5/5-{4.1249969, 4.9000000} 2.865 64.924
11 14.667 35 RASH. CEM. WITHOUT X EXT. KG/M KUB-5/5-{449.8119122, 533.7931034} 2.595 67.518
12 16,000 54 WATER CONSUMPTION LITER/M CUBE-4/5-{212.4000000, 241.2000000} 2.332 69.850
13 17.333 63 WEIGHT BET. MIXTURES KG-3/5-{2190.4213090, 2251.3919636} 2.199 72.049
14 18.667 eleven COEFF. VOID RUBBER-1/5-{0.4669118, 0.4723974} 2.146 74.195
15 20,000 20 IST. DENSITY CRUSHED STONE KG/M KUB-5/5-{2706.0000000, 2720.0000000} 2.146 76.341
16 21.333 25 BULK DENSITY OF CRUSHED STONE-5/5-{1428.0000000, 1450.0000000} 2.146 78.487
17 22.667 52 WATER CONSUMPTION LITER/M CUBE-2/5-{154.8000000, 183.6000000} 2.041 80.528
18 24,000 59 CHEMICAL. ADDITIVE LITER/M CUBE-4/5-{3.3127216, 4.1063608} 1.555 82.084
19 25.333 8 ROAD OF CRUSHED STONE-hard rocks 1.325 83.409
20 26.667 26 COEFF. G SLIDING ZEREN-1/5-{1.1000000, 1.1920000} 1.223 84.632
21 28,000 45 CRUSHED STONE CONSUMPTION KG/M KUB-5/5-{1194.4559078, 1238.5724560} 1.223 85.856
22 29.333 39 CONSUMPTION OF CEMENT KG/M CUBE-4/5-{334.9993730, 412.4996865} 0.955 86.811
23 30.667 74 CHEM. APP. PLASTIF. KG/M KUB-4/5-{3.3499937, 4.1249969} 0.955 87.766
24 32,000 44 CRUSHED STONE CONSUMPTION KG/M KUB-4/5-{1150.3393596, 1194.4559078} 0.894 88.660
25 33.333 1 ROAD OF CRUSHED STONE-granite 0.891 89.550
26 34.667 34 RASH. CEM. WITHOUT X EXT. KG/M KUB-4/5-{365.8307210, 449.8119122} 0.795 90.346
27 36,000 27 COEFF. G SLIDING ZEREN-2/5-{1.1920000, 1.2840000} 0.699 91.045
28 37.333 51 WATER CONSUMPTION LITER/M CUBE-1/5-{126.0000000, 154.8000000} 0.528 91.572
29 38.667 65 WEIGHT BET. MIXTURES KG-5/5-{2312.3626181, 2373.3332726} 0.504 92.077
30 40,000 3 ROCK OF CRUSHED STONE-limestone 0.482 92.559
31 41.333 6 BREED OF GRINDED STONE-tv.p, gran 0.467 93.026
32 42.667 31 RASH. CEM. WITHOUT X EXT. KG/M KUB-1/5-{113.8871473, 197.8683385} 0.464 93.490
33 44,000 36 CONSUMPTION OF CEMENT KG/M CUB-1/5-{102.4984326, 179.9987461} 0.418 93.908
34 45.333 71 CHEM. APP. PLASTIF. KG/M KUB-1/5-{1.0249843, 1.7999874} 0.418 94.326
35 46.667 56 CHEMICAL. ADDITIVE LITER/M CUBE-1/5-{0.9318039, 1.7254431} 0.361 94.687
36 48,000 58 CHEMICAL. ADDITIVE LITER/M CUBE-3/5-{2.5190823, 3.3127216} 0.359 95.046
37 49.333 10 ROAD OF CRUSHED STONE - crushed stone. 0.345 95.391
38 50.667 thirty COEFF. G SLIDING ZEREN-5/5-{1.4680000, 1.5600000} 0.335 95.726
39 52,000 38 CONSUMPTION OF CEMENT KG/M CUBE-3/5-{257.4990596, 334.9993730} 0.321 96.047
40 53.333 73 CHEM. APP. PLASTIF. KG/M KUB-3/5-{2.5749906, 3.3499937} 0.321 96.368
41 54.667 42 CRUSHED STONE CONSUMPTION KG/M KUB-2/5-{1062.1062633, 1106.2228115} 0.280 96.648
42 56,000 28 COEFF. G SLIDING ZEREN-3/5-{1.2840000, 1.3760000} 0.272 96.921
43 57.333 43 CRUSHED STONE CONSUMPTION KG/M KUB-3/5-{1106.2228115, 1150.3393596} 0.272 97.193
44 58.667 29 COEFF. G SLIDING ZEREN-4/5-{1.3760000, 1.4680000} 0.264 97.457
45 60,000 2 BREED OF CRUSHED STONE-KNOWN 0.263 97.720
46 61.333 33 RASH. CEM. WITHOUT X EXT. KG/M KUB-3/5-{281.8495297, 365.8307210} 0.249 97.969
47 62.667 37 CONSUMPTION OF CEMENT KG/M CUBE-2/5-{179.9987461, 257.4990596} 0.237 98.206
48 64,000 72 CHEM. APP. PLASTIF. KG/M KUB-2/5-{1.7999874, 2.5749906} 0.237 98.443
49 65.333 7 RING 0.234 98.677
50 66.667 57 CHEMICAL. ADDITIVE LITER/M CUBE-2/5-{1.7254431, 2.5190823} 0.207 98.884
51 68,000 41 CRUSHED STONE CONSUMPTION KG/M KUB-1/5-{1017.9897151, 1062.1062633} 0.198 99.081
52 69.333 32 RASH. CEM. WITHOUT X EXT. KG/M KUB-2/5-{197.8683385, 281.8495297} 0.192 99.273
53 70.667 46 SAND CONSUMPTION KG/M CUB-1/5-{551.5449724, 756.8359779} 0.190 99.463
54 72,000 47 SAND CONSUMPTION KG/M CUBE-2/5-{756.8359779, 962.1269834} 0.153 99.616
55 73.333 9 ROAD OF CRUSHED STONE - crushed stone. 0.138 99.754
56 74.667 53 WATER CONSUMPTION LITER/M CUBE-3/5-{183.6000000, 212.4000000} 0.072 99.826
57 76,000 64 WEIGHT BET. MIXTURES KG-4/5-{2251.3919636, 2312.3626181} 0.058 99.884
58 77.333 15 COEFF. VOID RUBBER-5/5-{0.4888540, 0.4943396} 0.031 99.916
59 78.667 16 IST. DENSITY CRUSHED STONE KG/M KUB-1/5-{2650.0000000, 2664.0000000} 0.031 99.947
60 80,000 21 BULK DENSITY OF CRUSHED STONE-1/5-{1340.0000000, 1362.0000000} 0.031 99.978
61 81.333 66 VOLUME BET. Sweep M CUBE-1/5-{0.9700000, 0.9760000} 0.022 100,000
62 82.667 12 COEFF. VOID Rubble-2/5-{0.4723974, 0.4778829} 0.000 100,000
63 84,000 13 COEFF. VOID RUBBER-3/5-{0.4778829, 0.4833685} 0.000 100,000
64 85.333 14 COEFF. VOID RUBBER-4/5-{0.4833685, 0.4888540} 0.000 100,000
65 86.667 17 IST. DENSITY CRUSHED STONE KG/M KUB-2/5-{2664.0000000, 2678.0000000} 0.000 100,000
66 88,000 18 IST. DENSITY CRUSHED STONE KG/M KUB-3/5-{2678.0000000, 2692.0000000} 0.000 100,000
67 89.333 19 IST. DENSITY CRUSHED STONE KG/M KUB-4/5-{2692.0000000, 2706.0000000} 0.000 100,000
68 90.667 22 BULK DENSITY OF CRUSHED STONE-2/5-{1362.0000000, 1384.0000000} 0.000 100,000
69 92,000 23 BULK DENSITY OF CRUSHED STONE-3/5-{1384.0000000, 1406.0000000} 0.000 100,000
70 93.333 24 BULK DENSITY OF CRUSHED STONE-4/5-{1406.0000000, 1428.0000000} 0.000 100,000
71 94.667 48 SAND CONSUMPTION KG/M CUBE-3/5-{962.1269834, 1167.4179890} 0.000 100,000
72 96,000 49 SAND CONSUMPTION KG/M CUBE-4/5-{1167.4179890, 1372.7089945} 0.000 100,000
73 97.333 67 VOLUME BET. Sweep M CUBE-2/5-{0.9760000, 0.9820000} 0.000 100,000
74 98.667 68 VOLUME BET. Sweep M CUBE-3/5-{0.9820000, 0.9880000} 0.000 100,000
75 100,000 69 VOLUME BET. Sweep M CUBE-4/5-{0.9880000, 0.9940000} 0.000 100,000
This issue is discussed in more detail in [1].
Table13- The strength of the influence of factors on the behavior of the object __of modeling in the system-cognitive model INF4_
No. No.% Code Factor name Significance factor a, % Significance factor cumulatively, %
1 7.143 12 WEIGHT BET. MIXTURES KG 28.096 28.096
2 14.286 10 WATER CONSUMPTION LITER/M CUBE 12.411 40.507
3 21.429 eleven CHEMICAL. ADDITIVE LITER/MCU 8.789 49.296
4 28.571 1 BREED OF GROUND STONE 7.956 57.253
5 35.714 9 SAND CONSUMPTION KG/M CUBE 7.470 64.723
6 42.857 13 VOLUME BET. SWEEP M CUBE 7.123 71.845
7 50,000 7 CONSUMPTION OF CEMENT KG/M CUBE 5.177 77.023
8 57.143 14 CHEM. APP. PLASTIF. KG/M CUBE 5.177 82.200
9 64.286 6 RASH. CEM. WITHOUT X EXT. KG/M CUBE 4.636 86.836
10 71.429 8 CRUSHED STONE CONSUMPTION KG/M CUBE 3.096 89.932
eleven 78.571 5 COEFF. G SLIDING GRAINS 3.016 92.948
12 85.714 2 COEFF. VOID STONE 2.351 95.299
13 92.857 3 IST. DENSITY CRUSHED STONE KG/M CUBE 2.351 97.649
14 100,000 4 BULK DENSITY OF CRUSHED STONE 2.351 100,000
Table 17 shows that approximately 28% of the total influence on the behavior of the modeled object is due to the weight of the concrete mixture, another 12% of the influence is due to water consumption, and chemical additives have a relatively smaller influence: about 9%. Thus, these three factors in total, i.e. 21% of all factors provide approximately 50% of the total influence on the object of modeling, and 50% of the most significant factors give a total of 77% of the influence. The strongest factor: "Weight of the concrete mix", is about 12 times stronger than the weakest one: "True density of crushed stone".
Degree of determinism of classes and classification scales
This issue is discussed in more detail in [1]. Table 18 presents the initial data for constructing the cumulative curve in Figure 40 in [1].
Table 18 shows what proportion of the total degree of determinism of all classes each class has. The degree of conditionality by the values of the factors of different future states of the modeling object, corresponding to the classes, is quite significantly different from each other.
For example, only 16% of the most rigidly deterministic classes have a total of approximately 50% degree of determinism, and 50% of the most deterministic classes provide about 90% of the total determinism of all classes.
Table14- The degree of determinism of classes in the CK-model INF3
No. No.% Code Class name Significance, % Significance cumulative, %
1 1.031 62 K1 - COEFF. CHEM. SUPPLEMENTS-5/5-{0.9, 0.9} 5.933 5.933
2 2.062 67 K2 - COEFF. ACCOUNTING H.D. STRONG-5/5-{1.1, 1.2} 5.933 11.867
3 3.093 47 BRAND ACTIVITY OF CEMENT-5/5-{480.0, 500.0} 4.038 15.904
4 4.124 39 GRADE ON VODONEPR-W2 3.749 19.654
5 5.155 84 STAND. SAND RUB-2/5-{252.0, 320.4} 3.090 22.744
6 6.186 34 FROST GRADE-F50 3.049 25.793
7 7.216 83 STAND. SAND RUB-1/5-{183.7, 252.0} 3.008 28.801
8 8.247 56 WATER PERMEABILITY-4/5-{200.0, 220.0} 2.925 31.726
9 9.278 95 TOTAL COST OF FSG RUB/M CUBE-3/5-{1730.0, 2041.2} 2.637 34.363
10 10.309 43 BRAND ACTIVITY OF CEMENT-1/5-{400.0, 420.0} 2.307 36.671
11 11,340 89 STAND. PLASTIF. RUB-2/5-{63.0, 90.1} 2.307 38.978
12 12.371 17 STRENGTH GRADE-2/5-{190.0, 280.0} 2.184 41.162
13 13.402 57 WATER PERMEABILITY-5/5-{220.0, 240.0} 2.143 43.304
14 14.433 96 TOTAL COST OF FSG RUB/M CUB-4/5-{2041.2, 2352.4} 2.143 45.447
15 15.464 69 MARK BETH. MIXTURES M KG/SM KV-2/5-{190.0, 280.0} 2.101 47.548
16 16.495 16 STRENGTH GRADE-1/5-{100.0, 190.0} 2.060 49.609
17 17.526 74 STAND. CEMENT RUB-2/5-{547.3, 774.3} 2.019 51.628
18 18.557 75 STAND. CEMENT RUB-3/5-{774.3, 1001.3} 1.937 53.564
19 19.588 68 MARK BETH. MIXTURES M KG/SM KV-1/5-{100.0, 190.0} 1.895 55.459
20 20.619 23 STANDARD BRAND-P3 1.772 57.231
21 21.649 49 W/C WATER-CEMENT RATIO-2/5-{0.6, 0.8} 1.731 58.962
22 22.680 90 STAND. PLASTIF. RUB-3/5-{90.1, 117.2} 1.731 60.692
23 23.711 24 STANDARD BRAND-P4 1.607 62.299
24 24.742 79 STAND. RUBBER-2/5-{807.2, 892.1} 1.607 63.906
25 25.773 80 STAND. RUBBER-3/5-{892.1, 977.0} 1.607 65.513
26 26.804 18 STRENGTH GRADE-3/5-{280.0, 370.0} 1.566 67.079
27 27.835 50 W/C WATER-CEMENT RATIO-3/5-{0.8, 0.9} 1.566 68.644
28 28.866 70 MARK BETH. MIXTURES M KG/SM KV-3/5-{280.0, 370.0} 1.566 70.210
29 29.897 81 STAND. RUBBER-4/5-{977.0, 1062.0} 1.442 71.652
30 30.928 22 STANDARD BRAND-P2 1.401 73.053
31 31.959 88 STAND. PLASTIF. RUB-1/5-{35.9, 63.0} 1.319 74.372
32 32.990 73 STAND. CEMENT RUB-1/5-{320.3, 547.3} 1.236 75.608
33 34.021 6 STRENGTH CLASS COMPRESSION-B20 1.112 76.720
34 35.052 78 STAND. RUBBER-1/5-{722.2, 807.2} 1.071 77.792
35 36.082 2 STRENGTH CLASS COMPRESSION-IN-7.5 0.989 78.780
36 37.113 5 STRENGTH CLASS COMPRESSION-B15 0.989 79.769
37 38.144 35 FROST GRADE-F75 0.989 80.758
38 39.175 52 W/C WATER-CEMENT RATIO-5/5-{1.1, 1.2} 0.989 81.747
39 40.206 76 STAND. CEMENT RUB-4/5-{1001.3, 1228.3} 0.989 82.736
40 41.237 94 TOTAL COST OF FSG RUB/M CUB-2/5-{1418.8, 1730.0} 0.948 83.684
41 42.268 48 W/C WATER-CEMENT RATIO-1/5-{0.4, 0.6} 0.906 84.590
42 43.299 51 W/C WATER-CEMENT RATIO-4/5-{0.9, 1.1} 0.906 85.496
43 44.330 thirty FROST GRADE-F100 0.865 86.362
44 45.361 41 GRADE ON VODONEPR-W6 0.865 87.227
45 46.392 8 STRENGTH CLASS COMPRESSION-B25 0.824 88.051
46 47.423 31 FROST GRADE-F150 0.824 88.875
47 48.454 4 STRENGTH CLASS COMPRESSION-B12.5 0.742 89.617
48 49.485 7 STRENGTH CLASS COMPRESSION-B22.5 0.742 90.358
49 50.515 21 BRAND FOR SURFACE-J1 0.700 91.059
50 51.546 54 WATER PERM EABILITY-2/5-{160.0, 180.0} 0.700 91.759
51 52.577 91 STAND. PLASTIF. RUB-4/5-{117.2, 144.4} 0.700 92.460
52 53.608 32 FROST GRADE-F200 0.536 92.995
53 54.639 40 GRADE ON VODONEPR-W4 0.536 93.531
54 55.670 97 TOTAL COST OF FSG RUB/M CUB-5/5-{2352.4, 2663.6} 0.536 94.067
55 56.701 42 GRADE ON VODONEPR-W8 0.494 94.561
56 57.732 19 STRENGTH GRADE-4/5-{370.0, 460.0} 0.412 94.973
57 58.763 71 MARK BETH. MIXTURES M KG/SM KV-4/5-{370.0, 460.0} 0.412 95.385
58 59.794 25 BRAND ON DOBOOKL.-P5 b. us. 0.371 95.756
59 60.825 37 GRADE ON VODONEPR-W10 0.330 96.086
60 61.856 53 WATER PERMEABILITY-1/5-{140.0, 160.0} 0.330 96.415
61 62.887 82 STAND. RUBBER-5/5-{1062.0, 1146.9} 0.330 96.745
62 63.918 92 STAND. PLASTIF. RUB-5/5-{144.4, 171.5} 0.288 97.033
63 64.948 36 GRADE ON VODONEPR-W 4 0.247 97.281
64 65.979 87 STAND. SAND RUB-5/5-{457.1, 525.5} 0.247 97.528
65 67.010 9 STRENGTH CLASS COMPRESSION-B30 0.206 97.734
66 68.041 10 STRENGTH CLASS COMPRESSION-B35 0.206 97.940
67 69.072 29 BRAND ON DOBOOKL.-SZh2 0.206 98.146
68 70.103 58 K1 - COEFF. CHEM. ADDITIVE-1/5-{0.8, 0.8} 0.165 98.311
69 71.134 63 K2 - COEFF. ACCOUNTING H.D. STRONG-1/5-{1.0, 1.0} 0.165 98.475
70 72.165 77 STAND. CEMENT RUB-5/5-{1228.3, 1455.3} 0.165 98.640
71 73.196 1 STRENGTH CLASS COMPRESSION-B12.5 0.124 98.764
72 74.227 eleven STRENGTH CLASS COMPRESSION-B40 0.124 98.888
73 75.258 20 STRENGTH GRADE-5/5-{460.0, 550.0} 0.124 99.011
74 76.289 28 BRAND ON DOBOOKL.-SZh-2 0.124 99.135
75 77.320 38 GRADE ON VODONEPR-W12 0.124 99.258
76 78.351 72 MARK BETH. MIXTURES M KG/SM KV-5/5-{460.0, 550.0} 0.124 99.382
77 79.381 14 STRENGTH CLASS COMPRESSION-M150 0.082 99.464
78 80.412 15 STRENGTH CLASS COMPRESSION-M200 0.082 99.547
79 81.443 26 BRAND ON DOBOOKL.-P5 b.us 0.082 99.629
80 82.474 27 BRAND ON DOBOOKL.-P5 (b us) 0.082 99.712
81 83.505 33 FROST GRADE-F25 0.082 99.794
82 84.536 93 TOTAL COST OF FSG RUB/M CUB-1/5-{1107.6, 1418.8} 0.082 99.876
83 85.567 3 STRENGTH CLASS COMPRESSION-IN 12.5 0.041 99.918
84 86.598 12 STRENGTH CLASS IN COMPRESSION-M 100 0.041 99.959
85 87.629 13 STRENGTH CLASS COMPRESSION-M100 0.041 100,000
86 88.660 44 BRAND ACTIVITY OF CEMENT-2/5-{420.0, 440.0} 0.000 100,000
87 89.691 45 BRAND ACTIVITY OF CEMENT-3/5-{440.0, 460.0} 0.000 100,000
88 90.722 46 BRAND ACTIVITY OF CEMENT-4/5-{460.0, 480.0} 0.000 100,000
89 91.753 55 WATER PERMEABILITY-3/5-{180.0, 200.0} 0.000 100,000
90 92.784 59 K1 - COEFF. CHEM. SUPPLEMENTS-2/5-{0.8, 0.8} 0.000 100,000
91 93.814 60 K1 - COEFF. CHEM. SUPPLEMENTS-3/5-{0.8, 0.9} 0.000 100,000
92 94.845 61 K1 - COEFF. CHEM. SUPPLEMENTS-4/5-{0.9, 0.9} 0.000 100,000
93 95.876 64 K2 - COEFF. ACCOUNTING H.D. STRONG-2/5-{1.0, 1.1} 0.000 100,000
94 96.907 65 K2 - COEFF. ACCOUNTING H.D. STRONG-3/5-{1.1, 1.1} 0.000 100,000
95 97.938 66 K2 - COEFF. ACCOUNTING H.D. STRONG-4/5-{1.1, 1.1} 0.000 100,000
96 98.969 85 STAND. SAND RUB-3/5-{320.4, 388.8} 0.000 100,000
97 100,000 86 STAND. SAND RUB-4/5-{388.8, 457.1} 0.000 100,000
Table 19 provides information on the degree of determinism of classes by the values of factors in the INF3 system-cognitive model. The degree of determination of classification scales is the average of the degree of determination of their gradations.
Table15- The degree of determinism of classification scales
in the system-cognitive model I [NF3
Degree Degree
determinism, determinism
No. No.% Code Name of the classification scale % cumulative, %
1 6.250 2 STRENGTH GRADE 7.044 7.044
2 12,500 6 GRADE ACTIVITY OF CEMENT 7.044 14.088
3 18,750 12 STAND. CEMENT RUB 7.044 21.132
4 25,000 14 STAND. SAND RUB 7.044 28.176
5 31.250 15 STAND. PLASTIF. RUB 7.044 35.221
6 37,500 16 TOTAL COST OF FSG RUB/M CUBE 7.044 42.265
7 43.750 7 W/C WATER CEMENT REL. 6,770 49.034
8 50,000 8 WATER PERMEABILITY 6,770 55.804
9 56.250 9 K1 - COEFF. CHEM. ADDITIVES 6,770 62.574
10 62,500 10 K2 - COEFF. ACCOUNTING H.D. ON STRENGTH 6,770 69.343
11 68.750 11 MARK BETH. MIXTURES M KG/SM KV 6,770 76.113
12 75,000 13 STAND. rubble RUB 6.724 82.837
13 81.250 4 BRAND FOR FROST. 5,870 88.707
14 87,500 5 BRAND ON VODONEPR 5.032 93.739
15 93.750 3 COMFORTABLE BRAND 3.913 97.652
16 100,000 1 STRENGTH CLASS WHEN COMPRESSED 2.348 100,000
The discussion and conclusions are given in [1] and it is inappropriate to cover them in detail in this article.
В заключение остается добавить, что в работе [1] все желающие могут ознакомиться с полным вариантом данной статьи на русском языке.
REFERENCES
1. Lutsenko E.V. Automated system-cognitive analysis of the influence of concrete composition on its physical and mechanical properties and cost // July 2023,
DOI: 10.13140/RG.2.2.14701.56804, License CC_BY_4.0,
https://www.researchgate.net/publication/372591270
