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1360030

全球建筑市场人工智慧 (AI) - 2023-2030

Global Artificial Intelligence (AI) in Construction Market - 2023-2030

出版日期: 2023年10月11日 | 出版商:

DataM Intelligence | 英文 182 Pages | 商品交期: 约2个工作天内

价格

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简介目录

概述：

2022年，全球建筑市场人工智慧（AI）规模达到6亿美元，预计2030年将达到78亿美元，2023-2030年预测期间复合年增长率为33.7%。

建筑业对人工智慧研发的投资增加正在推动创新以及新的人工智慧工具和解决方案的开发。持续的城市化趋势正在增加对建筑项目的需求，而人工智慧可以帮助更有效地满足这些需求。人工智慧有助于确保建筑项目遵守建筑规范和法规，降低代价高昂的法律问题的风险。

例如，2022 年 11 月 7 日，Trimble 和 Exyn Technologies 正在合作开发自主施工测量技术。该解决方案将结合波士顿动力公司的 Spot 机器人、由 ExynAI 提供支援的 Exyn 的 ExynPak 和 Trimble 的 X7 全站仪，以在复杂的施工环境中实现完全自主的任务。可以对收集的资料进行分析并与建筑资讯模型进行比较，以进行品质和进度监控。

亚太地区是全球人工智慧（AI）建筑市场成长的地区之一，覆盖超过1/4的市场，该地区许多国家正在经历快速城市化，导致建筑项目激增。人工智慧可以帮助高效管理和优化这些大型专案。自动化和人工智慧（AI）技术可以透过处理劳动密集、重复性任务来弥补这一差距。该地区各国政府正在大力投资交通、能源和住房基础建设。

动态：

降低生产成本带动市场

具有人工智慧功能的机器人和机械可以自动化耗时且重复的操作，从而降低对体力劳动的需求和随之而来的劳动成本。人工智慧演算法可以利用资料分析来优化供应、设备和劳动力的分配方式，从而减少浪费并更有效地利用资源。人工智慧可以追踪建筑设备的健康状况并预测维护需求，从而减少代价高昂的故障和停机时间。

根据埃森哲最近的一项研究，到 2035 年，采用人工智慧可能会使建筑业的利润增加 71%。埃森哲强调了在建筑业采用人工智慧的巨大潜在好处。人工智慧有能力提高效率、降低成本、提高安全性并增强建设专案的决策能力。

艾默生的研究表明，到专案结束时，设计和施工阶段创建的初始资料中有 30% 会遗失。它允许专案团队将实际费用与预算成本进行比较，确保专案保持在财务限制范围内。确定可以优化或降低成本的领域可以显着节省成本并提高专案获利能力。持续的成本监控可以洞察潜在的成本超支或财务风险，从而实现主动的风险管理策略。

加强安全措施的需求

人工智慧，特别是机器学习演算法，可以分析历史资料来预测潜在的安全问题。透过识别模式和趋势，人工智慧可以预测事故或不安全情况，从而采取预防措施。由于先进的传感器和物联网 (IoT) 设备的普及，建筑工地可以获得有关工人活动、机器操作、环境等的大量实时信息，并且这些资料可以通过人工智能进行处理和分析，以发现潜在危险和安全漏洞。

根据 NCCER 2021 年的报告，机器人流程自动化 (RPA) 透过与机器互动实现任务自动化。在建筑领域，人工智慧驱动的自动化有助于消除危险任务，降低与体力劳动相关的风险。人工智慧分析大型资料集并得出智慧结论的能力可用于评估机械、工作订单和供应链，这种预测分析功能为工作流程优化和安全措施提供了宝贵的见解。

市场上机器学习和深度学习演算法的不断进步

机器学习和深度学习演算法的进步使人工智慧系统能够分析大量的施工资料，使其更有能力识别模式、优化流程并为决策提供有价值的见解。边缘人工智慧在设备本地或网路边缘处理资料，增强了人工智慧系统在远端或资源有限的施工环境中的反应和效率。

例如，2021 年 8 月 17 日，人工智慧建筑技术新创公司 Togal.ai 进入市场，旨在彻底改变建筑估算流程。该公司声称，其软体可以透过准确测量每个房间的大小和定价建筑成本来自动化和加快估算过程，这项任务通常需要数週时间，但使用 Togal 可以在几秒钟内完成。

有限的历史数据和劳动力存储

人工智慧系统严重依赖高品质的相关资料。在施工过程中，由于资讯来源多样、资料格式各异以及用于训练人工智慧演算法的历史资料有限，因此获取干净且一致的资料可能具有挑战性。建设项目通常涉及敏感和专有资讯。在实施人工智慧解决方案时，保护这些资料免受网路威胁并确保遵守资料隐私法规可能是一项重大挑战。

据美国建筑商和承包商协会称，2022 年预计将短缺约 665,000 名建筑工人，这一预测是基于 ABC 对行业状况的研究以及考虑了通货膨胀和建筑支出等因素的独特模型。预计将有 120 万名建筑工人放弃工作岗位，这是造成这一短缺的重要因素，而这种流失加剧了该行业本已严重的熟练劳动力短缺问题。

Increased investment in AI research and development within the construction industry is driving innovation and the development of new AI-powered tools and solutions. The ongoing trend of urbanization is increasing the demand for construction projects and AI can help meet these demands more efficiently. AI assists in ensuring that construction projects adhere to building codes and regulations, reducing the risk of costly legal issues.

For instance, on 7 November 2022, Trimble and Exyn Technologies are collaborating on the development of autonomous construction surveying technology. The solution will combine Boston Dynamics' Spot robot, Exyn's ExynPak powered by ExynAI and Trimble's X7 total station to enable fully autonomous missions within complex construction environments. The collected data can be analyzed and compared to Building Information Models for quality and progress monitoring.

Asia-Pacific is among the growing regions in the global artificial intelligence (AI) in construction market covering more than 1/4th of the market and many countries in the region are experiencing rapid urbanization, leading to a surge in construction projects. AI can help manage and optimize these large-scale projects efficiently. Automation and artificial intelligence (AI) technologies may bridge this gap by handling labor-intensive, repetitive tasks. Governments in the area are making significant investments in the construction of transportation, energy and housing infrastructure.

Dynamics:

Reducing Production Costs Drives the Market

Robots and machinery with AI capabilities can automate time-consuming and repetitive operations, hence lowering the demand for manual labor and the accompanying labor costs. AI algorithms may utilize data analysis to optimize how supplies, equipment and labor are allocated, resulting in less waste and more effective resource use. Artificial intelligence can track the health of construction equipment and forecast maintenance requirements, reducing costly breakdowns and downtime.

According to a recent study by Accenture, the adoption of AI can potentially increase the construction industry's profits by 71% by 2035. Accenture highlights the significant potential benefits of adopting AI in the construction industry. AI has the power to increase efficiency, reduce costs, improve safety and enhance decision-making in construction projects.

Emerson's study revealed that 30% of initial data created during the design and construction phases is lost by the time the project ends. It allows project teams to compare actual expenses with the budgeted costs, ensuring that the project remains within financial constraints. Identifying areas where costs can be optimized or reduced can lead to significant cost savings and improved project profitability. Continuous cost monitoring provides insights into potential cost overruns or financial risks, enabling proactive risk management strategies.

The Demand for Enhanced Safety Measures

AI, particularly machine learning algorithms, can analyze historical data to predict potential safety issues. By recognizing patterns and trends, AI can anticipate accidents or unsafe conditions, allowing for preventive measures. An abundance of real-time information about worker activities, machine operation, the environment and more is made available at construction sites because of the spread of sophisticated sensors and Internet of Things (IoT) devices and this data can be processed and analyzed by AI to find potential dangers and security breaches.

According to NCCER in 2021, Robotic process automation(RPA) enables the automation of tasks through interactions with machines. In construction, AI-driven automation helps eliminate dangerous tasks, reducing the risks associated with manual labor. AI's ability to analyze large datasets and draw intelligent conclusions is leveraged to assess machinery, work orders and supply chains and this predictive analytics capability provides valuable insights into workflow optimization and safety measures..

Rising Advancements in Machine Learning and Deep Learning Algorithms in the Market

Advancements in machine learning and deep learning algorithms have enabled AI systems to analyze vast amounts of construction data, making them more capable of identifying patterns, optimizing processes and providing valuable insights for decision-making. Edge AI which processes data locally on devices or at the edge of the network, enhances the responsiveness and efficiency of AI systems in remote or resource-constrained construction environments.

For instance, on 17 August 2021, Togal.ai, an Artificial Intelligence construction technology startup, entered the market, aiming to revolutionize the estimating process in construction. The company claims its software can automate and expedite the estimating process by accurately measuring the size of each room and pricing the cost of construction, a task that typically takes weeks but can be completed in seconds with Togal.

Limited Historical Data and Storage of Labours

AI systems rely heavily on high-quality and relevant data. In construction, obtaining clean and consistent data can be challenging due to the diverse sources of information, variations in data formats and limited historical data for training AI algorithms. Construction projects often involve sensitive and proprietary information. Protecting this data from cyber threats and ensuring compliance with data privacy regulations can be a significant challenge when implementing AI solutions.

According to the Associated Builders and Contractors in 2022, there is a shortage of about 665,000 construction workers is anticipated and this forecast is based on a study of the industry's state by ABC and a unique model that takes into account things like inflation and construction spending. The predicted 1.2 million construction employees who are anticipated to abandon their positions is a significant factor in this shortfall and this attrition exacerbates the already critical shortage of skilled labor in the industry.

Segment Analysis:

The global artificial intelligence (AI) in construction market is segmented based on offerings, deployment type, organization size, end-user and region.

Scalability of Cloud-Based AI Platforms Boosts the Growth of the Market

Cloud-based AI platforms can easily scale to accommodate the needs of construction projects of varying sizes and this scalability allows construction companies to adapt AI resources to their specific requirements. Cloud solutions often work on a pay-as-you-go basis, reducing the need for substantial capital expenditures upfront. Because of their affordability, AI technology is now available to a wider spectrum of construction enterprises.

For instance, on 9 September 2023, U.S. technology company Nvidia formed partnerships with two major Indian conglomerates, Reliance Industries and Tata Group, to establish artificial intelligence infrastructure in India. Nvidia will provide the necessary computing power to Reliance for constructing a cloud-based AI infrastructure platform, with Jio overseeing infrastructure management and customer engagement. Additionally, Tata Consultancy Services in collaboration with Nvidia, will develop generative AI applications and a supercomputer.

Geographical Penetration:

Adoption of Digital Platform Boosts the Market

North America is dominating the global artificial Intelligence in construction market and the region is home to some of the world's leading tech companies and research institutions, making it a hub for AI development and this access to cutting-edge technology fuels innovation in construction. The construction industry is undergoing a digital transformation, with AI playing a crucial role. Companies are increasingly recognizing the value of AI-driven solutions for efficiency, cost savings and competitiveness.

For instance, on 06 May 2021, Procore Technologies, a prominent construction management software provider, acquired INDUS.AI, a company known for its AI-powered analytics platform tailored for the construction industry and this acquisition enhances Procore's capabilities by introducing computer vision technology, aiming to improve efficiency, safety and profitability for owners, general contractors and specialty contractors.

Competitive Landscape

The major global players in the market include: Building System Planning, Inc., SAP SE, Autodesk, Inc., NVIDIA Corporation, International Business Machines Corp, Microsoft Corporation, Inc. oracle Corporation, Dassault Systems SE, Aurora Computer Services Limited and PTC Inc.

COVID-19 Impact Analysis

The pandemic accelerated the construction industry's digital transformation efforts. To minimize disruptions caused by lockdowns and social distancing measures, many construction companies turned to AI and digital technologies to enable remote work, collaboration and project management. AI-powered tools for project planning, scheduling and monitoring became essential in ensuring projects continued despite the challenges posed by the pandemic.

Safety concerns heightened during the pandemic, leading to an increased focus on AI-driven safety solutions. AI-based systems for monitoring social distancing, mask-wearing and site occupancy helped construction companies adhere to health and safety guidelines. AI also played a role in contactless site access control and temperature screening. The pandemic exposed vulnerabilities in global supply chains, affecting the availability and delivery of construction materials.

The pandemic exposed vulnerabilities in global supply chains, affecting the availability and delivery of construction materials. AI-powered supply chain management tools helped construction firms adapt to changing conditions by providing real-time visibility into material availability and alternative sourcing options. Travel restrictions and limited on-site personnel, AI-enabled remote inspection and monitoring solutions gained importance. Drones, equipped with AI-powered cameras, were used for site inspections and progress monitoring.

AI Impact

AI analyzes architectural designs to optimize energy efficiency, material use and cost-effectiveness, leading to environmentally friendly and cost-saving designs. AI algorithms can assess potential risks and uncertainties in construction projects, helping project managers make informed decisions. AI-driven project management tools can optimize project schedules, allocate resources efficiently and manage project budgets, reducing delays and cost overruns.

In order to provide insights into project performance and enable data-driven decision-making, AI can evaluate project data in real-time. Real-time monitoring of building sites by AI-powered technologies can assist in identifying safety risks and avert accidents. AI can analyze historical data to predict potential risks and issues, allowing for proactive risk mitigation. AI-based computer vision systems can perform real-time quality inspections, ensuring that construction work meets quality standards and reducing the cost of rework.

For instance, on 14 December 2022, PCL Construction entered a multi-year partnership with AI Clearing, focusing on its Solar division. This partnership aims to enhance the management of solar projects by implementing AI Clearing's AI Surveyor solution. AI Surveyor is a construction technology platform powered by artificial intelligence and advanced GIS analytics. It automates the progress reporting of construction infrastructure, using drone-captured data to provide daily progress reports, monitor Key Performance Indicators and flag potential deviations.

Russia- Ukraine War Impact

The conflict may disrupt supply chains for construction materials and equipment, leading to delays and shortages. AI-driven supply chain management systems may become more critical in navigating these disruptions by providing real-time visibility into material availability and alternative sourcing options. The geopolitical instability resulting from the war can create economic uncertainty, affecting construction projects' funding and investment.

The conflict's impact on the global economy can affect construction projects worldwide. Economic slowdowns can lead to budget cuts for construction projects, impacting the adoption of AI technologies. International conflicts can strain research collaborations between countries, affecting the exchange of knowledge and expertise in AI for construction. Government priorities in both Russia and Ukraine may shift towards defense and security, potentially reducing investments in civil infrastructure and technology sectors, including AI for construction.

By Offerings

Solutions
Services

By Deployment Type

Cloud
On-Premise

By Organization Size

Small and Medium-sized Enterprises
Large Enterprises

By End-User

Residential
Institutional
Commercials
Others

By Region

North America
- U.S.
- Canada
- Mexico

Europe
- Germany
- UK
- France
- Italy
- Russia
- Rest of Europe

South America
- Brazil
- Argentina
- Rest of South America

Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific

Middle East and Africa

Key Developments

In April 2023, Autodesk, Inc. and VietNam National Construction Consultants signed a Memorandum of Understanding to establish a strategic partnership. Autodesk, Inc. will provide guidance and assistance to VC Group in adopting digital design and construction consultancy technologies, in alignment with the Vietnamese Government's decision to promote the use of Building Information Modeling (BIM) in the construction sector.
In June 2022, Siemens and NVIDIA have expanded their partnership to enable the industrial metaverse and enhance the use of AI-driven digital twin technology in industrial automation. They plan to connect Siemens Xcelerator, an open digital business platform, with NVIDIA Omniverse, a platform for 3D design and collaboration.
In July 2020, Autodesk, Inc. signed a definitive agreement to acquire Pype, a cloud-based construction project management software provider. Pype's suite of software uses artificial intelligence and machine learning to automate critical construction workflows, such as submittals and closeouts.

Why Purchase the Report?

To visualize the global artificial intelligence (AI) in construction market segmentation based on offerings, deployment type, organization size, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of artificial intelligence (AI) in construction market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global artificial intelligence (AI) in construction market report would provide approximately 69 tables, 65 figures and 182 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Offerings
3.2. Snippet by Deployment Type
3.3. Snippet by Organization Size
3.4. Snippet by End-User
3.5. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Reducing Production Costs Drives the Market
  - 4.1.1.2. The Demand for Enhanced Safety Measures
  - 4.1.1.3. Rising Advancements in Machine Learning and Deep Learning Algorithms in the Market
- 4.1.2. Restraints
  - 4.1.2.1. Limited Historical Data and Storage of Labours
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Offerings

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
- 7.1.2. Market Attractiveness Index, By Offerings
7.2. Solutions*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Services

8. By Deployment Type

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 8.1.2. Market Attractiveness Index, By Deployment Type
8.2. Cloud*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. On-Premise

9. By Organization Size

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 9.1.2. Market Attractiveness Index, By Organization Size
9.2. Small and Medium-sized Enterprises*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Large Enterprises

10. By End-User

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.1.2. Market Attractiveness Index, By End-User
10.2. Residential*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Institutional
10.4. Commercials
10.5. Others

11. By Region

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 11.1.2. Market Attractiveness Index, By Region
11.2. North America
- 11.2.1. Introduction
- 11.2.2. Key Region-Specific Dynamics
- 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
- 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.2.7.1. U.S.
  - 11.2.7.2. Canada
  - 11.2.7.3. Mexico
11.3. Europe
- 11.3.1. Introduction
- 11.3.2. Key Region-Specific Dynamics
- 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
- 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.3.7.1. Germany
  - 11.3.7.2. UK
  - 11.3.7.3. France
  - 11.3.7.4. Italy
  - 11.3.7.5. Russia
  - 11.3.7.6. Rest of Europe
11.4. South America
- 11.4.1. Introduction
- 11.4.2. Key Region-Specific Dynamics
- 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
- 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.4.7.1. Brazil
  - 11.4.7.2. Argentina
  - 11.4.7.3. Rest of South America
11.5. Asia-Pacific
- 11.5.1. Introduction
- 11.5.2. Key Region-Specific Dynamics
- 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
- 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.5.7.1. China
  - 11.5.7.2. India
  - 11.5.7.3. Japan
  - 11.5.7.4. Australia
  - 11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
- 11.6.1. Introduction
- 11.6.2. Key Region-Specific Dynamics
- 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offerings
- 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment Type
- 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis

13. Company Profiles

13.1. Building System Planning, Inc.*
- 13.1.1. Company Overview
- 13.1.2. Product Portfolio and Description
- 13.1.3. Financial Overview
- 13.1.4. Key Developments
13.2. SAP SE
13.3. Autodesk, Inc.
13.4. NVIDIA Corporation
13.5. International Business Machines Corp
13.6. Microsoft Corporation, Inc.
13.7. Oracle Corporation
13.8. Dassault Systems SE
13.9. Aurora Computer Services Limited
13.10. PTC Inc.

LIST NOT EXHAUSTIVE

14. Appendix

14.1. About Us and Services
14.2. Contact Us

02-2729-4219

+886-2-2729-4219

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