The construction industry accounts for seven percent of the global workforce, progressing slowly in the applications of technologies to improve productivity. The quality has risen to a level wherein technologies like AI and machine learning can be applied, in methods like those entailed below:
• Prevention of cost overshoots and risk mitigation
In large-scale enterprises, due to unforeseen circumstances, projects are subjected to quality, safety, and financial risks even in the most efficient of management systems. AI partnered with machine learning can create realistic schedules, identify vulnerabilities, and provide a solution for risk factors holistically for the project.
• Better sequencing design
The process of constructing a building is a complex symmetry of inter-connected professionals and different areas of expertise which needs the harmonious working of the labor force. Generative design with the aid of the building information modeling software is the solution to avoid the phenomenon of sequence clashing and achieve successful completion.
• Productive project planning and remote construction
In industry 4.0, various projects can be scanned to identify sub-projects with the help of AI, machine learning, and neural networks. The scans identify projects requiring immediate manual labor and differentiate them from scheduled, repetitive tasks, which are allotted to machines. Projects can be distinguished based on priorities, the budget requirement of labor and small units to be built by machines.
• Addressing job shortages
AI and machine learning can identify the usefulness of the machines and laborers in a particular site. The systems plot out the accurate distribution of labor and machinery across different job sites, controlling the budgets, providing job opportunities, and allotting work precisely.
• Construction sites are data libraries
The variety of construction sites bestow an enormous load of data for AI and advanced machines, which can be used for later reference. The accumulated data can impact on the productivity of the subsequent project, and with every new project, the technology gets better and potential problems faced can be traced back with collected data and prevented.
The change in technology is inevitable, and the potential for costs to reduce is up to 20 percent with higher productivity with Al augmented with machine learning.