Last date to apply:

Development of a Methodology for Evaluating a Machine-Data-Driven Compaction Indicator for Vibratory Plate Compactors

About Us:    

Husqvarna Construction is a world-leading supplier of equipment and solutions for construction, demolition, and light compaction applications. Husqvarna has built strong global market leadership and one of the industry's most recognized brands.

At the Compaction & Concrete Vibration R&D team at Husqvarna Construction, we design, test, and build the next generation of compaction and concrete vibration equipment — working closely between simulation, physical testing, and product development to bring new machine concepts to life.

Background:

Vibratory plate compactors are a core part of Husqvarna Construction's product range, spanning both forward and reversible plate models used extensively across construction settings. Soil compaction is one of the most fundamental quality parameters in ground preparation and civil construction. While Husqvarna already offers a compaction indicator within its portfolio, the broader landscape of compaction indication is still evolving, and there remains significant scope to explore data-driven approaches that leverage the full range of machine parameters available on modern plate compactors.

It is currently unclear which machine-derived signal, or combination of signals, most reliably reflects true compaction state, and there is no established methodology for deriving a data-driven compaction indicator from them. 

         

Thesis Work Objective:

The purpose of this thesis is therefore to develop a methodology for designing and evaluating a machine-data-driven compaction indicator for Husqvarna Construction's forward and reversible plate compactors. This involves instrumenting the machines with relevant sensing modalities — such as drive power consumption, acceleration, or acoustic signature — and collecting data under varying soil conditions and pass counts, with the aim of identifying which signals, or combinations thereof, can reliably indicate compaction state.

Activities:

• Literature study of existing compaction indicators and associated sensors
• Analysis and selection of candidate sensors/machine parameters for instrumentation
• Data collection across varying soil conditions, pass counts, and machine types
• Data evaluation and pattern analysis
• Development of a methodology for a data-driven compaction indicator, based on the finding

Relevant knowledge/background:    

• Structural dynamics & vibrations
• Mechatronics & instrumentation                         
• Data-driven methods (data analysis, ML, or optimization)

Preferred Masters Program:  

                  
Mechatronics/ Machine Design/ Systems, Control and Mechatronics or anything similar

Thesis Level: Master (30 ECTS points)

Starting date: January 2027

Number of students: 2 students

Last application date: 15 Nov 2026

Location: Husqvarna Construction, Jonsered, Sweden

Contact: Adithya Srikantha Dath, Mechanical Engineer, [email protected]