Report includes a snapshot of active projects where DASNY delivers some level of project management oversight.

Success.ai’s Construction Data for Building Materials & Construction Industry Leaders in Europe provides a reliable dataset tailored for businesses seeking to connect with leaders in the European construction and building materials sectors. Covering contractors, suppliers, architects, and project managers, this dataset offers verified profiles, firmographic insights, and decision-maker contacts.

With access to over 700 million verified global profiles and data from 70 million businesses, Success.ai ensures that your outreach, market analysis, and strategic partnerships are powered by accurate, continuously updated, and AI-validated information. Backed by our Best Price Guarantee, this solution empowers you to engage effectively with the construction industry across Europe.

Why Choose Success.ai’s Construction Data?

1. Verified Contact Data for Industry Leaders

   • Access verified work emails, phone numbers, and LinkedIn profiles of construction executives, project managers, and building material suppliers.
   • AI-driven validation ensures 99% accuracy, optimizing outreach and minimizing inefficiencies in communication.

2. Comprehensive Coverage Across Europe’s Construction Sector

   • Includes profiles from major construction hubs such as Germany, France, the UK, Italy, and Spain, covering a diverse range of projects and organizations.
   • Gain insights into regional construction trends, material sourcing strategies, and large-scale project developments.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, market expansions, material innovations, and project announcements.
   • Stay ahead of market trends to align your strategies with evolving industry needs.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with decision-makers, contractors, architects, and engineers in Europe’s construction sector.
• 70M Business Profiles: Access firmographic data, including company sizes, revenue ranges, and geographic locations.
• Decision-Maker Contacts: Connect directly with CEOs, procurement officers, and project leads driving construction projects and material procurement.
• Industry Insights: Gain visibility into supply chain networks, sustainable building initiatives, and innovative construction techniques.

Key Features of the Dataset:

1. Leadership Profiles in Construction

   • Identify and connect with leaders responsible for major construction projects, material sourcing, and architectural planning.
   • Target professionals making decisions on vendor selection, project timelines, and compliance.

2. Advanced Filters for Precision Campaigns

   • Filter companies by industry segment (commercial construction, residential, infrastructure), geographic location, or revenue size.
   • Tailor campaigns to align with regional construction challenges, such as sustainability, cost management, or urbanization.

3. Firmographic Insights and Project Data

   • Access data on company structures, project scopes, and market positioning to refine your targeting strategy.
   • Use these insights to identify high-value prospects and uncover new business opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with construction stakeholders.

Strategic Use Cases:

1. Sales and Vendor Development

   • Offer construction materials, tools, or technology solutions to procurement teams and project managers in the construction industry.
   • Build relationships with vendors and contractors seeking innovative solutions to streamline operations and reduce costs.

2. Market Research and Competitive Analysis

   • Analyze trends in material usage, construction technologies, and sustainable practices to guide product development and marketing strategies.
   • Benchmark against competitors to identify market gaps, emerging needs, and high-growth opportunities.

3. Partnership Development and Supply Chain Optimization

   • Engage with companies seeking partnerships for large-scale projects, material sourcing, or technology integration.
   • Foster alliances that drive efficiency, quality, and innovation in construction projects.

4. Recruitment and Workforce Solutions

   • Target HR professionals and hiring managers recruiting skilled workers, architects, or engineers for ongoing and upcoming projects.
   • Provide staffing services, training platforms, or workforce optimization tools tailored to the construction sector.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality construction data at competitive prices, ensuring strong ROI for your marketing, sales, and bu...

New school projects (Capacity) and Capital Improvement Projects (CIP) currently under Construction.

Comprehensive database showcasing planned and in-construction infrastructure projects worldwide, uncovering technology, capital flows, people in upcoming mega investments and business opportunities for market research and business intelligence

Dataset Overview

This dataset is a simulated dataset containing 1,000 entries of construction cost estimates. It is designed for use in predictive modeling, machine learning, and business analytics, particularly in the construction and project management domains. The dataset includes both numerical and textual data, providing opportunities for hybrid modeling approaches that combine structured data and natural language processing.

The primary objective of this dataset is to facilitate modeling of construction cost estimation while considering policy-driven adjustments (discounts or markups). It can be used to analyze and predict how various factors, such as material costs, labor costs, and policy reasons, affect final project estimates.

Feature Descriptions

1) Material_Cost (numeric):

• Represents the cost of materials required for a construction project, measured in USD.
• Range: $10,000–$50,000.
• This is one of the primary cost components in project estimation.

2) Labor_Cost (numeric):

• Represents the labor cost involved in completing the project, measured in USD.
• Range: $5,000–$30,000.
• Indicates the cost of human resources required for the construction.

3) Profit_Rate (numeric):

• Represents the percentage of profit added to the combined material and labor costs.
• Range: 10%–30% (5 distinct values: 10%, 15%, 20%, 25%, 30%).
• Reflects the margin a company expects from the project.

4) Discount_or_Markup (numeric):

• Represents the adjustment amount applied to the project cost, measured in USD.
• Range: -$10,000 to $10,000.
• A negative value indicates a discount, and a positive value indicates a markup, based on the

5) Policy_Reason (text):

• A textual explanation (up to 50 words) justifying the discount or markup applied to the cost. Examples include: "This client is new but has potential for growth." "The client is a repeat customer, deserving a loyalty discount." "Strategic importance of this client justifies special pricing." This feature is ideal for natural language processing (NLP) applications.

6) Total_Estimate (numeric): - The final estimated project cost, calculated as:

    (Material_Cost + Labor_Cost) × (1 + Profit_Rate/100) + Discount_or_Markup

• Represents the overall cost of the project after including the profit margin and any adjustments

China Construction: Project Revenue data was reported at 26,800,738.923 RMB mn in 2022. This records an increase from the previous number of 26,245,381.074 RMB mn for 2021. China Construction: Project Revenue data is updated yearly, averaging 3,940,958.660 RMB mn from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 26,800,738.923 RMB mn in 2022 and a record low of 116,953.690 RMB mn in 1990. China Construction: Project Revenue data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Construction Sector – Table CN.EE: Construction Enterprise: All.

This dataset captures 1300 key performance and planning variables from large-scale infrastructure construction projects. It includes features such as task duration, labor availability, equipment usage, material costs, and constraint scores related to site and resource conditions. Additionally, risk levels, dependencies, and start constraints are represented to reflect the complexities of real-world project scheduling and resource planning.

Total dollar value and number of projects either in review, pending construction, in construction, or in closure aggregated into California counties, once every two weeks since September 2013. A construction project moves through the Department of Health Care Access and Information (HCAI) in four stages - In Review; Pending Construction Start; Under Construction; and In Closure. A project can only be in one of these four stages at any time. Additional data when available will be added to this dataset approximately once every two weeks.

As of November 2024, several high-value construction projects to develop residential buildings had been added to the Australian Construction Industry Forum (ACIF) Major Projects Database in Australia, with commencement dates between 2018 and 2028. The Former West End Brewery Site Mixed Used Development and Rosehill Race Course Residential & School Redevelopment projects had the highest values across the thirty major construction projects at around *********** Australian dollars, respectively.

As of November 2024, several high-value construction projects to develop wind farms and clean energy infrastructure among other projects had been added to the Australian Construction Infrastructure Forum (ACIF) Major Projects Database in Australia, with commencement dates between 2025 and 2030. The Elanora offshore wind farm stages 1 & 2 project had the highest value across the thirty major construction projects at around ** billion Australian dollars, with an expected start date of June 2029.

Context

Building construction projects generate huge amounts of data that can be leveraged to understand improvements in efficiency, cost savings, etc. There are several digital apps on the market that helps construction project managers keep track of the details of the process.

Content

This is a simple data set from a number of construction sites generated from project management field apps that are used for quality, safety a and site management.

Essential there are two files in this data set: - Forms – generated from check list for quality/safety/site management - Tasks – which is an action item typically used for quality snags/defects or safety issues.

Acknowledgements

This data set was donated by Jason Rymer, a BIM Manager from Ireland who was keen to see more construction-related data online to be used to learn

Inspiration

The goal of this data set is to help construction industry professionals to learn how to code and process data.

The Capital Projects Database reports information at the project level on discrete capital investments from the Capital Commitment Plan.Each row is uniquely identified by its Financial Management Service (FMS) ID, and contains data pertaining to the sponsoring and managing agency.

To explore the data, please visit Capital Planning Explorer

For additional information, please visit A Guide to The Capital Budget. Current version: 25exec

Introduction:

This project is aimed to put some light upon the problem of predicting which of the incoming projects and their budgets are accurate scheduling the end of the construction and its resources. The initial issue to solve is to get valid data of real constructions with their delay reported.

Of course, large construction companies have huge lists of observations of this kind. But in this sector local circumstances are highly relevant, like the socioeconomic moment or the location of each construction process, as they affect to viability, prices and HHRR. So, even for these companies, having big “clean” data doesn’t mean that this data will be helpful without expert data preprocessing.

Raw data description:

As an Expert Model, the relevant raw data is provided by the Data Scientist to train the model. This is an strategic decision that helps to use the scarce data from the field effectively as testing data. Taking into account that the Data Scientist on command for this study is an Architect and works as Project Manager in the construction sector, we expect that his experience is valuable for creating a rich and expert dataset with observations of good and bad constructions characteristics in terms of its delay. The method used for creating this Train Dataset is a controlled normal distribution (using “numpy.random”). Variables are controlled by restricting the “centre” of the distribution and its standard deviation. Of course, every normal distribution captures an intuition of “good” or “bad” characteristics in terms of project planning.

Summary of main results.

The concept "True delay" depends on the delays and the duration, assigning a threshold. It is considered TRUE DELAY time terms higher than 15% of the total duration of the construction project. So, the threshold is assigned on a new boolean variable “DELAYED”, the one used as target. With ML ensemble ,we have increased accuracy by 2% over the most accurate algorithm alone (68.6% acc Random Forest) by giving each of the algorithms the right of flagging the project as a “possible delayed project”. But this strategy obviously tend to overfit the model, reducing its robustness. We have trained a ML Ensemble model to detect Delays in a construction only with some previous conditions of the construction contract. As the Train Dataset have higher proportion of “DELAYED” observations, this machine will tend to over detect false positives.

Conclusions.

This study and the resulting tool would be helpful for a “second opinion” in management auditions. Due to the changing socio-economic variables​ (material and human resources prices and fluctuations in the building market), ​the data has a short-term validity​. So it is strongly advised to have a maintenance plan for this kind of models. The maintenance should be driven by an expert in Data Science with experience in the construction field.

This data set contains DOT construction project information. The data is refreshed nightly from multiple data sources, therefore the data becomes stale rather quickly.

Russia Number of Federal Construction Projects: In Progress data was reported at 44.000 Unit in 2014. This records a decrease from the previous number of 58.000 Unit for 2013. Russia Number of Federal Construction Projects: In Progress data is updated yearly, averaging 67.000 Unit from Dec 1998 (Median) to 2014, with 17 observations. The data reached an all-time high of 214.000 Unit in 2005 and a record low of 5.000 Unit in 1998. Russia Number of Federal Construction Projects: In Progress data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Global Database’s Russian Federation – Table RU.EC003: Federal Construction Projects: by Sector.

Information related to construction projects for determining the project duration

List of currently active infrastructure projects including description and high level schedule and budget range.

List of CIP Road Construction Projects in the County.

This is the dataset included in the paper "Causes of time and cost overruns in construction projects: a scoping review"

Abstract he construction industry is one of the industrial sectors with the lowest rates of fulfilment of contract deadlines, especially in developing countries. This fact has been the focus of considerable discussions seeking to identify the causes of the delays. The main purpose of this paper is to use factor analysis to identify the factors that are correlated with delay, contemplating exclusively residential real estate projects and using a city in the Brazilian Amazon as a case study. Based on the database from the government agency that authorises constructions in the city of Belém (City Planning Department - Secretaria Municipal de Urbanismo, SEURB) and data from construction companies, the study investigated 274 construction projects from the past 11 years. Factor analysis and work with the variables that can be identified and measured in the initial phase of the project, i.e., during the feasibility study, demonstrate that the physical characteristics of the apartments and the construction project are the primary causes for variations in construction delays; these causes have not yet been reported in the literature. We hope that the results of this study will contribute to more consistent forecasting of construction time, minimising the risk of delays.