ARTIFICIAL INTELLIGENCE AND ITS USE IN ENGINEERING

Fernando Alcoforado*

Abstract: This article aims to present the Artificial Intelligence tools already in use in Engineering.

Keywords: Artificial Intelligence and its different types. Artificial Intelligence tools already in use in Engineering.

1. Introduction

This article aims to present the Artificial Intelligence tools already in use in the areas of Engineering that contribute to significantly improving the efficiency, precision and innovation of construction and production processes, as well as pointing out creative and effective solutions for today's technological challenges. In this sense, the different types of Artificial Intelligence will be presented, the current Engineering management tools with the use of ERPs and Artificial Intelligence with its applications in Engineering.

2. Artificial intelligence and its different types

What is Artificial Intelligence (AI)? The article A Inteligência Artificial na Engenharia: entenda sua importancia (Artificial Intelligence in Engineering: understand its importance) [1] informs that AI is defined as a field of study that originates from the following areas:

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All of these areas, in a multidisciplinary way, have as their main objective the construction of systems that are configured with a mode of operation and intelligent behavior in order to carry out activities and process information with excellence equivalent to or greater than that of a human being. To this end, AI is based on three pillars: reasoning, learning and perception. Reasoning is the logical process of understanding and making decisions. Learning is the ability of a machine to learn from past experiences and adjust its behavior accordingly. Perception is the ability of a machine to process information from the external world [1].

Artificial Intelligence (AI) is a computational technology or a set of technologies such as artificial neural networks, algorithms and learning systems whose objective is to imitate human mental capabilities, such as: reasoning, environmental perception and decision-making capacity [2]. The technology is developed with the aim that machines can solve a series of problems, covering everything from the great complexity of government and industry management to the daily tasks of modern men and women. To do this, AI uses sophisticated learning technology, allowing it to learn from a large set of data and act on its own. The general objective of AI is to create machines that can operate with the same level of cognitive capacity as humans, or even surpass them [2].

In the words of the computer scientist who coined the term, John McCarthy, Artificial Intelligence is “the science and engineering of producing intelligent systems” [3]. It is the technology used to make machines behave like humans when performing manual activities, making decisions, understanding data and even creating content (most recent innovation). Machines are equipped with data and programmed to learn from it, dividing information into layers and recognizing patterns. The article Inteligência Artificial na educa??o: benefícios e desafios (Artificial Intelligence in education: benefits and challenges) informs that AI has given rise to several types. Among them, the following can be highlighted [3]:

? Generative AI: generates new data and samples (such as images, texts and music) similar to a training data set. Examples: ChatGPT and DALL-E. ChatGPT is a chat bot and virtual assistant developed by OpenAI and released on November 30, 2022, based on large language models that allows users to refine and direct a conversation for duration, format, style, level of detail and desired language. DALL?E, DALL?E 2, and DALL?E 3 are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as "prompts".

? Discriminative AI: classifies data into predefined categories based on specific features. It is able to detect objects, recognize patterns, collect, analyze and present information. Examples: facial recognition, adaptive learning platform and school data platform.

? Reactive AI: deals only with current information and does not maintain a memory of previous data. She makes decisions based on predefined rules and is not able to learn or adapt to new situations.

? Knowledge-Based AI: uses a database of human knowledge to make decisions and solve problems, using logical rules. Example: medical diagnosis system.

? Machine Learning AI: can learn and continuously improve based on data. Your learning can be supervised, unsupervised or reinforcement. Example: spam email identification system.

? Deep Learning AI: subfield of Machine Learning, uses deep artificial neural networks to learn complex data representations. Examples: image and speech recognition, machine translation and text processing.

? Natural Language Processing (NLP) AI: focuses on the interaction between computers and human language. Examples: chatbots, virtual assistants, machine translation and sentiment analysis.

? Autonomous AI: capable of operating autonomously and making decisions without human intervention. Examples: autonomous cars and robots.

3. Current Engineering management tools using ERP

ERP is nothing more than business management software that serves to automate manual processes, store data and unify the visualization of results [4]. ERP stands for Enterprise Resource Planning – translating from English, “Company Resource Planning”. ERP is nothing more than business management software that serves to automate manual processes, store data and unify the visualization of results. ERP is widely used mainly in civil construction. ERP for civil construction is business management software developed specifically to meet the needs of companies in the construction sector. It offers an integrated solution to manage all areas of the company, from planning to execution.

The article Os 10 melhores ERP′s da Constru??o Civil (The 10 best ERPs for Civil Construction) informs that the best ERPs for civil construction are the following [4]:

Koper ERP

Koper ERP is construction management software that offers solutions for various areas, such as HR, administration, quality, purchasing, engineering, finance, supplies and sales. It is aimed at construction companies or developers looking for an integrated, complete and easy-to-use management solution. With Koper ERP, it is possible to integrate all areas of construction into a single system, which facilitates communication and exchange of information between teams, as well as allowing an overview of the works. This enables more accurate and efficient decision-making, reduced costs and rework, better integration of the construction team and time savings. The software also offers technical support and constant updates to ensure the best system performance and security.

Protheus ERP

It is business management software from TOTVS, which can be customized to meet the needs of the construction industry. Protheus is business management software (ERP) developed by the Brazilian company TOTVS. It is one of the best-known and most used management systems in Brazil, with more than 30 years of history and thousands of clients in various sectors. Protheus is robust software that offers many functionalities and advanced business management features. Some of the main features of Protheus ERP include: a) Feasibility studies; b) Budgets and quotations; c) Planning; d) Management of teams and resources; e) Monitoring of execution.

Sienge ERP

It is a complete and integrated solution to manage all processes of a construction company, from financial management to project execution. Sienge is a popular option in the construction ERP market, with a broad customer base. The software also offers technical support and training for users, ensuring that companies can make the most of all its features and benefits. Some of the main features of Sienge ERP include: a) Budgets; b) Measurements; c) Control of supplies and equipment; d) Orders; e) Integration with BIM, which is a process that involves the generation and management of digital representations of the physical and functional characteristics of buildings and other physical assets. BIM is supported by several tools, technologies and contracts.

Gerencia Obras ERP?

Gerencia Obras is a business management software (ERP) developed by the Brazilian company Gerencia Obras?Tecnologia. The system is specifically designed for companies in the construction industry and offers a complete and integrated solution to manage all company processes, from the financial part to project execution.?

UAU ERP

UAU is business management software (ERP) developed by the Brazilian company Globaltec. The system is specific for companies in the construction industry and offers a complete and integrated solution to manage all company processes, from the financial part to project execution. Its main advantages are: a) Reprint invoices; b) Check availability of units for sales; c) Access payment statements; d) Register personalized services.

Omie ERP

Omie is a Brazilian ERP software, the system is aimed at financial, accounting, sales, inventory and production management, in addition to offering resources for project and construction management. In the case of civil construction, Omie ERP allows contract management, issuance of invoices, stock control of materials and equipment, cost and budget management, as well as resources for payroll management and time control. The system also offers project management tools, allowing the monitoring of stages, deadlines and budgets, as well as task control and communication between teams.

Mega ERP

Mega is an ERP developed by the Brazilian company Mega Sistemas. Specifically designed for companies in different sectors, including construction, Mega offers a complete and integrated solution to manage all company processes, from the financial part to project execution. Some of the main features of Mega ERP include: a) Composition; b) Security; c) Budget; d) Contracts; e) Notes.

EVOP ERP

Evop is a Brazilian ERP. Created for construction companies, Evop offers a complete and integrated solution to manage all company processes, from the financial part to project execution. Main features of EVOP: a) Integration with BIM; b) Financial; c) Schedule; d) Budgets; e) Works Diary.

Obra Prima ERP

Obra Prima was designed for companies in the construction industry, offering a complete solution to manage all company processes, from the administrative part to project execution. Main features of ERP: a) Measurements; b) Risk analysis; c) Budget with profit margin calculations per stage; d) Schedule; e) Purchases.

Obrafit ERP

Obrafit is software that was developed specifically to meet the needs of companies operating in the civil construction and engineering sector. It has specific modules for controlling works and projects, such as budget, schedule, execution monitoring, measurements and billing. It is an efficient solution for companies looking for a specific business management tool for the civil construction and engineering sector.

Overall, an ERP for construction is very important as it allows companies to manage all areas in an integrated and efficient way, making it easier to make informed decisions and ensure that all activities are carried out on time and within budget. It is worth remembering that this is just a list of some ERP options for construction, and choosing the best system for a company depends on several factors, such as size, budget, specific needs and available resources [4].

4. Artificial Intelligence and its applications in Engineering

How is Artificial Intelligence being used in Engineering? AI is being applied in several areas of engineering, from project design to execution. One of the main applications is in data analysis, allowing engineers to process large volumes of information quickly and accurately. Artificial Intelligence in Engineering is already a reality and can be applied in different areas bringing more functionality, practicality, time savings, among many other benefits. In this case, it is similar to what an ERP for Civil Engineering does through the integration of information from different areas of Engineering, such as computing, civil construction, production, energy, among others, which can already benefit from the use of artificial intelligence. When we talk about Artificial Intelligence in Engineering, the premise is to build systems capable of analyzing data accurately and contributing to increasingly assertive decision-making.

The article A Inteligência Artificial na Engenharia: entenda sua importancia (Artificial Intelligence in Engineering: understand its importance) [1] informs that the main uses of AI are the following [1]:

Machine Learning (ML)

Machine learning is a data analysis method that automates the construction of analytical models in systems. One of the main parts of AI's work is to enable systems to learn from data, identify patterns and make decisions with minimal human intervention. Thus, there is a reduction in operational costs and a reduction in errors related to decision-making, mainly because it becomes possible to analyze complex and voluminous data through the creation of algorithms that project ideal scenarios (error-free). In other words, it is as if AI guarantees a continuous improvement process based on data.

Deep Learning (DL)

Delving deeper into the concept of Machine Learning, Deep Learning is an even more advanced technology and, in addition to learning from data, it is capable of crossing data to generate insights and adapt to different scenarios and needs. One of the practical benefits of DL is fraud prevention. In financial systems, for example, it has the autonomy to identify suspicious patterns of behavior and block possible fraud or errors with the potential to result in even more serious errors.

Project monitoring

Artificial Intelligence in Engineering can also be used to monitor projects in order to improve construction management, optimize processes and speed up stages, for example. AI finds patterns for all phases of a project, which facilitates its management as a whole, from the initiation and planning phase to the analysis of processes, methodologies and closure. This is an efficient way to avoid small errors throughout the project and save time, considerably minimizing any chance of delay.

Logistics

Artificial Intelligence in Engineering also has features and benefits related to logistics, especially when we talk about materials management, supply chain and even 3D printing of works, warehouses and the like. Through software and virtual reality glasses, AI makes it possible to “be inside” that space and check its conditions before it is even completed. In this context, AI can be applied through an automation system to cross-reference information related to stock, inventory patterns and storage information, such as ideal room temperature, climate and the like.

Reporting and strategic decision-making

Artificial Intelligence in Engineering uses data, monitors its patterns, cross-references this information to generate insights and almost completely reduces errors arising from human labor. Therefore, naturally this entire ecosystem facilitates decision-making involving the project, regardless of the phase it is in. This is because the system does all the analytical work. That is, filter the database, map processes, eliminate errors and deliver to the person responsible for the project only what is relevant (having a reliable basis). This makes it easier for the manager to make assertive and safe decisions in situations such as calculations of works and projects, definition of performance indicators, budget analysis, among others.

The article Como Engenheiros Podem Utilizar a Inteligência Artificial em Seus Projetos (How Engineers Can Use Artificial Intelligence in Their Projects) [5] reports that Artificial Intelligence (AI) is revolutionizing several areas of engineering, becoming a valuable tool for engineers around the world. This technology can be applied to a wide range of projects, from the design and analysis of structures to the optimization of manufacturing processes. Artificial Intelligence can be used: 1) in Data Analysis and Forecasts; 2) in Design Optimization; 3) in Process Control and Automation; 4) in Simulations and Modeling; and, 5) in Predictive Maintenance [5].

Data Analysis and Forecasts

AI is particularly powerful when it comes to data analysis and predictions. Engineers can use AI algorithms to analyze large data sets, identify trends, anomalies, and hidden insights. For example:

? In Civil Engineering, AI can be used to predict the behavior of structures under different climatic or load conditions.

? In Production Engineering, engineers can use AI to predict when machines need maintenance, minimizing unplanned downtime.

? In Electrical Engineering, AI can help in the analysis of energy consumption data, helping to optimize the use of resources.

Design Optimization

AI can be a powerful ally in the design optimization process. AI-based optimization algorithms can explore a wide range of variables and constraints to find the best solutions. This is particularly useful in product engineering and industrial design, where efficiency is essential.

? In Automotive Engineering, AI can be used to optimize vehicle design in terms of aerodynamics, fuel consumption and safety.

? In Materials Engineering, AI can help identify materials with ideal properties for specific applications.

Process Control and Automation

AI can also be applied to improve process control and automation in Engineering. AI-based control systems can make real-time decisions based on data and feedback, improving efficiency and accuracy across multiple processes:

? In Manufacturing Engineering, AI can optimize the control of machines and robots in production lines, reducing errors and increasing efficiency.

? In Traffic Engineering, AI can be used to improve the control of traffic lights and public transport systems, reducing congestion.

Simulations and Modeling

AI is also valuable for creating complex simulations and models. Engineers can use AI to create detailed predictive models that represent complex, real-life systems. This is useful in several areas:

? In Software Engineering, AI can be used to simulate the behavior of complex systems before implementation.

? In Aerospace Engineering, AI can assist in flight simulation and aircraft design.

Predictive Maintenance

Predictive maintenance is another crucial application of AI in Engineering. Through real-time data analysis, AI algorithms can predict when equipment or systems are about to fail, enabling intervention before serious problems occur:

? In Power Engineering, AI can monitor wind turbines to identify wear and tear and schedule maintenance.

? In Mechanical Engineering, AI can predict when machine components need to be replaced.

In the energy area, Artificial Intelligence can be used to optimize the distribution and management of the electrical grid, ensuring the supply of energy efficiently and safely. Another promising application of AI in Electrical Engineering is in the development of smart grids. These smart grids integrate renewable energy sources, energy storage systems and advanced communications technologies to optimize energy distribution and management. The article Aplica??es de Inteligência Artificial Generativa no setor de energia (Applications of Generative Artificial Intelligence in the energy sector) [6] reports that generative Artificial Intelligence (AI) is emerging as an innovative tool in several sectors, and the energy sector is no exception. With the growing need for smart and efficient solutions to address global energy challenges, generative AI offers a range of promising applications. From optimizing electrical grids to creating realistic simulations for developing new energy sources, this technology is revolutionizing the way we produce, distribute and consume energy [6].

Generative AI can be used to create optimized designs for solar farms and wind turbines. By considering variables such as wind patterns, solar incidence and terrain topography, generative algorithms can generate layouts that maximize energy generation. This not only increases efficiency but also helps in mitigating environmental impacts [6]. Unlike conventional AI systems, which are designed to answer questions or perform specific tasks based on existing data, generative AI is capable of creating new data, images, text or other materials that did not exist before. It learns from a set of data and can generate original, realistic outputs that resemble what was learned. One of the most critical areas for the energy sector is the optimization of electrical networks. Generative AI can be applied to create simulations and models that improve the operational efficiency and reliability of networks.

Generative algorithms can analyze historical consumption data, weather conditions, and other factors to predict future energy demands with high accuracy [6]. This allows companies to adjust energy production and distribution more intelligently, reducing costs and avoiding overloads. Generative AI can analyze a variety of data, including market trends, seasonal patterns, weather conditions and geopolitical events, to predict future energy prices. These forecasts are valuable to network operators, traders and consumers, allowing them to make informed decisions about contracts, investments and consumption. Maintaining energy infrastructures is essential to avoid failures and interruptions in supply. Generative AI can be employed in predictive maintenance, where algorithms analyze sensor data in real time to predict when equipment needs repairs or replacement. This reduces unplanned downtime, increases asset life, and improves the overall safety of operations [6].

Energy companies can use generative AI to simulate a variety of scenarios [6]. When planning large-scale energy storage integration, algorithms can simulate different configurations and capacities to identify the best approach. This saves time and resources by allowing companies to test various options virtually before making real investments. Generative Artificial Intelligence is playing an increasingly important role in transforming the energy sector. From optimizing electrical grids to designing renewable energy systems and price forecasting, its applications are diverse and promising. However, it is important to address challenges proactively, ensuring that the implementation of these technologies is ethical, transparent and focused on positive outcomes for society and the environment. With responsible use, generative AI has the potential to drive energy efficiency and accelerate the transition to a more sustainable energy future [6].

5. Conclusions

In summary, artificial intelligence offers a vast set of tools and techniques that can be applied in different fields of Engineering. Engineers who embrace AI in their projects can significantly improve efficiency, accuracy and innovation, becoming leaders in finding creative and effective solutions to today's technological challenges. Based on the above, the use of Artificial Intelligence in the areas of Engineering represents a step forward in relation to the previous practice with the use of ERPs in the management of projects and works.

REFERENCES

1. 90TI. Inteligência Artificial na engenharia: entenda sua importancia. Available on the website <https://noventa.com.br/inteligencia-artificial-na-engenharia-entenda-sua-importancia/ >.

2. ALCOFORADO, Fernando. How Artificial Intelligence and its softwares and smart algorithms work. Available on the website <https://www.dhirubhai.net/pulse/how-artificial-intelligence-its-softwares-smart-work-alcoforado-s7cgf/ >.?? ?

3. EDUCACIONAL. Inteligência Artificial na educa??o: benefícios e desafios. Available on the website <https://educacional.com.br/tecnologia-educacional/impactos-da-inteligencia-artificial-na-educacao/ >.

4. AIZEMBERG, Hari. Os 10 melhores ERP’s da constru??o civil. Available on the website <https://koper.com.br/10-melhores-erp-construcao-civil/ >.

5. CONEX?O EXATA. Como Engenheiros Podem Utilizar a Inteligência Artificial em Seus Projetos. Available on the website <https://www.dio.me/articles/como-engenheiros-podem-utilizar-a-inteligencia-artificial-em-seus-projetos >.

6. SILVA, Cristiane. Aplica??es de Inteligência Artificial Generativa no setor de energia. Available on the website <https://www.dio.me/articles/aplicacoes-de-inteligencia-artificial-generativa-no-setor-de-energia >.

* Fernando Alcoforado, awarded the medal of Engineering Merit of the CONFEA / CREA System, member of the SBPC- Brazilian Society for the Progress of Science, IPB- Polytechnic Institute of Bahia and of the Bahia Academy of Education, engineer from the UFBA Polytechnic School and doctor in Territorial Planning and Regional Development from the University of Barcelona, college professor (Engineering, Economy and Administration) and consultant in the areas of strategic planning, business planning, regional planning, urban planning and energy systems, was Advisor to the Vice President of Engineering and Technology at LIGHT S.A. Electric power distribution company from Rio de Janeiro, Strategic Planning Coordinator of CEPED- Bahia Research and Development Center, Undersecretary of Energy of the State of Bahia, Secretary of Planning of Salvador, is the author of the books Globaliza??o (Editora Nobel, S?o Paulo, 1997), De Collor a FHC- O Brasil e a Nova (Des)ordem Mundial (Editora Nobel, S?o Paulo, 1998), Um Projeto para o Brasil (Editora Nobel, S?o Paulo, 2000), Os condicionantes do desenvolvimento do Estado da Bahia (Tese de doutorado. Universidade de Barcelona,https://www.tesisenred.net/handle/10803/1944 , 2003), Globaliza??o e Desenvolvimento (Editora Nobel, S?o Paulo, 2006), Bahia- Desenvolvimento do Século XVI ao Século XX e Objetivos Estratégicos na Era Contemporanea (EGBA, Salvador, 2008), The Necessary Conditions of the Economic and Social Development- The Case of the State of Bahia (VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG, Saarbrücken, Germany, 2010), Aquecimento Global e Catástrofe Planetária (Viena- Editora e Gráfica, Santa Cruz do Rio Pardo, S?o Paulo, 2010), Amaz?nia Sustentável- Para o progresso do Brasil e combate ao aquecimento global (Viena- Editora e Gráfica, Santa Cruz do Rio Pardo, S?o Paulo, 2011), Os Fatores Condicionantes do Desenvolvimento Econ?mico e Social (Editora CRV, Curitiba, 2012), Energia no Mundo e no Brasil- Energia e Mudan?a Climática Catastrófica no Século XXI (Editora CRV, Curitiba, 2015), As Grandes Revolu??es Científicas, Econ?micas e Sociais que Mudaram o Mundo (Editora CRV, Curitiba, 2016), A Inven??o de um novo Brasil (Editora CRV, Curitiba, 2017),? Esquerda x Direita e a sua convergência (Associa??o Baiana de Imprensa, Salvador, 2018), Como inventar o futuro para mudar o mundo (Editora CRV, Curitiba, 2019), A humanidade amea?ada e as estratégias para sua sobrevivência (Editora Dialética, S?o Paulo, 2021), A escalada da ciência e da tecnologia e sua contribui??o ao progresso e à sobrevivência da humanidade (Editora CRV, Curitiba, 2022), a chapter in the book Flood Handbook (CRC Press,? Boca Raton, Florida United States, 2022), How to protect human beings from threats to their existence and avoid the extinction of humanity (Generis Publishing, Europe, Republic of Moldova, Chi?in?u, 2023), A revolu??o da educa??o necessária ao Brasil na era contemporanea (Editora CRV, Curitiba, 2023), Como construir um mundo de paz, progresso e felicidade para toda a humanidade (Editora CRV, Curitiba, 2024) and How to build a world of peace, progress and happiness for all humanity (Editora CRV, Curitiba, 2024).