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建築市場人工智能

市場調查報告書

商品編碼

1360030

全球建築市場人工智慧 (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: 約2個工作天內

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簡介目錄

概述：

2022年，全球建築市場人工智慧（AI）規模達到6億美元，預計2030年將達到78億美元，2023-2030年預測期間複合年成長率為33.7%。

建築業對人工智慧研發的投資增加正在推動創新以及新的人工智慧工具和解決方案的開發。持續的城市化趨勢正在增加對建築項目的需求，而人工智慧可以幫助更有效地滿足這些需求。人工智慧有助於確保建築項目遵守建築規範和法規，降低代價高昂的法律問題的風險。

例如，2022 年 11 月 7 日，Trimble 和 Exyn Technologies 正在合作開發自主施工測量技術。該解決方案將結合波士頓動力公司的 Spot 機器人、由 ExynAI 提供支援的 Exyn 的 ExynPak 和 Trimble 的 X7 全站儀，以在複雜的施工環境中實現完全自主的任務。可以對收集的資料進行分析並與建築資訊模型進行比較，以進行品質和進度監控。

亞太地區是全球人工智慧（AI）建築市場成長的地區之一，覆蓋超過1/4的市場，該地區許多國家正在經歷快速城市化，導致建築項目激增。人工智慧可以幫助高效管理和最佳化這些大型專案。自動化和人工智慧（AI）技術可以透過處理勞動密集、重複性任務來彌補這一差距。該地區各國政府正在大力投資交通、能源和住房基礎建設。

動態：

降低生產成本帶動市場

具有人工智慧功能的機器人和機械可以自動化耗時且重複的操作，從而降低對體力勞動的需求和隨之而來的勞動成本。人工智慧演算法可以利用資料分析來最佳化供應、設備和勞動力的分配方式，從而減少浪費並更有效地利用資源。人工智慧可以追蹤建築設備的健康狀況並預測維護需求，從而減少代價高昂的故障和停機時間。

根據埃森哲最近的一項研究，到 2035 年，採用人工智慧可能會使建築業的利潤增加 71%。埃森哲強調了在建築業採用人工智慧的巨大潛在好處。人工智慧有能力提高效率、降低成本、提高安全性並增強建設專案的決策能力。

艾默生的研究表明，到專案結束時，設計和施工階段創建的初始資料中有 30% 會遺失。它允許專案團隊將實際費用與預算成本進行比較，確保專案保持在財務限制範圍內。確定可以最佳化或降低成本的領域可以顯著節省成本並提高專案獲利能力。持續的成本監控可以洞察潛在的成本超支或財務風險，從而實現主動的風險管理策略。

加強安全措施的需求

人工智慧，特別是機器學習演算法，可以分析歷史資料來預測潛在的安全問題。透過識別模式和趨勢，人工智慧可以預測事故或不安全情況，從而採取預防措施。由於先進的感測器和物聯網 (IoT) 設備的普及，建築工地可以獲得有關工人活動、機器操作、環境等的大量即時資訊，並且這些資料可以通過人工智慧進行處理和分析，以發現潛在危險和安全漏洞。

根據 NCCER 2021 年的報告，機器人流程自動化 (RPA) 透過與機器互動實現任務自動化。在建築領域，人工智慧驅動的自動化有助於消除危險任務，降低與體力勞動相關的風險。人工智慧分析大型資料集並得出智慧結論的能力可用於評估機械、工作訂單和供應鏈，這種預測分析功能為工作流程最佳化和安全措施提供了寶貴的見解。

市場上機器學習和深度學習演算法的不斷進步

機器學習和深度學習演算法的進步使人工智慧系統能夠分析大量的施工資料，使其更有能力識別模式、最佳化流程並為決策提供有價值的見解。邊緣人工智慧在設備本地或網路邊緣處理資料，增強了人工智慧系統在遠端或資源有限的施工環境中的反應和效率。

例如，2021 年 8 月 17 日，人工智慧建築技術新創公司 Togal.ai 進入市場，旨在徹底改變建築估算流程。該公司聲稱，其軟體可以透過準確測量每個房間的大小和定價建築成本來自動化和加快估算過程，這項任務通常需要數週時間，但使用 Togal 可以在幾秒鐘內完成。

有限的歷史數據和勞動力存儲

人工智慧系統嚴重依賴高品質的相關資料。在施工過程中，由於資訊來源多樣、資料格式各異以及用於訓練人工智慧演算法的歷史資料有限，因此獲取乾淨且一致的資料可能具有挑戰性。建設項目通常涉及敏感和專有資訊。在實施人工智慧解決方案時，保護這些資料免受網路威脅並確保遵守資料隱私法規可能是一項重大挑戰。

據美國建築商和承包商協會稱，2022 年預計將短缺約 665,000 名建築工人，這一預測是基於 ABC 對行業狀況的研究以及考慮了通貨膨脹和建築支出等因素的獨特模型。預計將有 120 萬名建築工人放棄工作崗位，這是造成這一短缺的重要因素，而這種流失加劇了該行業本已嚴重的熟練勞動力短缺問題。

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

License/價格

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建築領域人工智慧的全球市場規模、佔有率、成長分析（按產品供應、按應用） - 2024-2031 年產業預測

2023-2030年全球建築業的人工智慧（AI）市場

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