Aparna Harichandran | Aarhus University (original) (raw)

Papers by Aparna Harichandran

Research paper thumbnail of Relevance of deep sequence models for recognising automated construction activities: a case study on a low-rise construction system

Journal of Information Technology in Construction

Recognising activities of construction equipment is essential for monitoring productivity, constr... more Recognising activities of construction equipment is essential for monitoring productivity, construction progress, safety, and environmental impacts. While there have been many studies on activity recognition of earth excavation and moving equipment, activity identification of Automated Construction Systems (ACS) has been rarely attempted. Especially for low-rise ACS that offers energy-efficient, cost-effective solutions for urgent housing needs, and provides more affordable living options for a broader population. Deep learning methods have gained a lot of attention because of their ability to perform classification without manually extracting relevant features. This study evaluates the feasibility of deep sequence models for developing an activity recognition framework for low-rise automated construction equipment. Time series acceleration data was collected from the structure to identify major operation classes of an ACS. Long Short Term Memory Networks (LSTM) were applied for ide...

Research paper thumbnail of A Critical Review on Methods for the Assessment of Trainees' Performance in Virtual Reality-based Construction Safety Training

Research paper thumbnail of State of the art Technologies that Facilitate Transition of Built Environment into Circular Economy

Proceedings of the 40th International Symposium on Automation and Robotics in Construction

Research paper thumbnail of Automated Recognition of Hand Gestures for Crane Rigging using Data Gloves in Virtual Reality

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

Research paper thumbnail of A Critical Review on Methods for the Assessment of Trainees' Performance in Virtual Reality-based Construction Safety Training

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

Virtual Reality (VR) is one of the transformative technologies that aid construction safety train... more Virtual Reality (VR) is one of the transformative technologies that aid construction safety training. This study isa systematic review of literature on estimating the performance of the trainees during VR-based construction safety training. The critical analysis of the selected literature identifiedseven focus areasof research, often overlapping with oneor more areas.The focus areas include hazard recognition training, personalised feedback,training methodimprovement, the effect of VR training, the efficacy of VR training, safety behaviour analysis, and automated safety analysis. Most studies focus on either training with existing scenarios for improving hazard recognition or enhancing the training method with the latest technologies or supplementary devices. The possibilities of automated data collection during VR training need to be further explored for quantitativelyestimatingthe participant performance and analysis of close calls or safety incidents.

Research paper thumbnail of A Robust Framework for Identifying Automated Construction Operations

Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC), 2020

Machine learning techniques have been successfully implemented for the identification of various ... more Machine learning techniques have been successfully implemented for the identification of various construction activities using sensor data. However, there are very few studies on activity recognition in the automated construction of low-rise residential buildings. Automated construction is faster than conventional construction, with minimal human involvement. This requires high accuracy of identification for monitoring its operations. This paper discusses the development and testing of machine learning classifiers to identify normal automated construction operations with high precision. The framework developed in this work involves decomposing the activity recognition problem into a hierarchy of learning tasks in which activities at the lower levels have more details. The top recognition level divides the equipment states into two classes: 'Idle' and 'Operations'. The second recognition level divides the 'operations' into major classes depending on the top-level activities performed by the equipment. The third recognition level further divides the activities into subclasses and so on. Since the number of classes and the similarity between them increase with the recognition level, identification becomes extremely difficult. The identification framework developed in this study classifies operations belonging to the parent class at each level in the hierarchy. The efficacy of this framework is demonstrated with a case study of a top-down modular construction system. In this construction system, the modules of a structural frame are assembled and lifted starting with the top floor followed by the ones below. The accelerometer data collected during top-down construction is used to identify the construction operations. The proposed framework shows superior performance over conventional identification using a flat list of classes.

Research paper thumbnail of A Conceptual Framework for Construction Safety Training using Dynamic Virtual Reality Games and Digital Twins

Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC), 2021

Research paper thumbnail of Development of Automated Top-Down Construction System for Low-rise Building Structures

International Journal of Industrialized Construction, 2020

Automation is the best solution for achieving high productivity and quality in the construction i... more Automation is the best solution for achieving high productivity and quality in the construction industry at reduced cost and time. The main objective of this study is to develop an economical automated construction system (ACS) for low-rise buildings. The incremental development of the construction system and the structural system through different versions of laboratory prototypes are described in this paper. These ACS prototypes adopt a top-down construction method. This method involves the building of the structural system step by step from the top floor to the bottom floor by connecting and lifting structural modules. ACS prototype 1 consist of wooden structural modules and electric motor system. ACS prototype 2 has a highly automated custom designed hydraulic motor system to construct steel structural frame. ACS prototype 3 is a partially automated system where the steel structural modules are connected manually. These prototypes were evaluated on the basis of function, cost an...

Research paper thumbnail of Sensor Data Interpretation in Bridge Monitoring—A Case Study

Frontiers in Built Environment, 2020

Research paper thumbnail of Inferring Construction Activities from Structural Responses Using Support Vector Machines

Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC), 2018

Research paper thumbnail of Equipment activity recognition and early fault detection in automated construction through a hybrid machine learning framework

Computer-Aided Civil and Infrastructure Engineering

Research paper thumbnail of A Conceptual Framework for Construction Safety Training using Dynamic Virtual Reality Games and Digital Twins

38th International Symposium on Automation and Robotics in Construction (ISARC 2021), 2021

The construction industry is suffering from a high rate of accidents that significantly affect th... more The construction industry is suffering from a high rate of accidents that significantly affect the overall performance of projects. Compared to the conventional safety training methods, Virtual Reality (VR) games offer a more immersive and interactive learning experience for the participants. However, training scenarios in most of the existing VR games lack complex tasks and the realistic environment required for actual construction. This work proposes a conceptual framework for dynamically updating VR training games through information streaming from the digital twins. The training scenarios in VR games are automatically created from the project intent information, project status knowledge, safety regulations, and historical knowledge provided by the digital twins. Therefore, construction workers can be trained in realistic training environments with relevant tasks they soon afterwards pursue. Dynamic VR training is expected mutually beneficial for enhanced digital twins and period...

Research paper thumbnail of A hierarchical machine learning framework for the identification of automated construction

J. Inf. Technol. Constr., 2021

A robust monitoring system is essential for ensuring safety and reliability in automated construc... more A robust monitoring system is essential for ensuring safety and reliability in automated construction. Activity recognition is one of the critical tasks in automated monitoring. Existing studies in this area have not fully exploited the potential for enhancing the performance of machine learning algorithms using domain knowledge, especially in problem formulation. This paper presents a hierarchical machine learning framework for improving the accuracy of identification of Automated Construction System (ACS) operations. The proposed identification framework arranges the operations to be identified in the form of a hierarchy and uses multiple classifiers that are organized hierarchically for separating the operation classes. It is tested on a laboratory prototype of an ACS, which follows a top-down construction method. The ACS consists of a set of lightweight and portable machinery designed to automate the construction of the structural frame of low-rise buildings . Accelerometers wer...

Research paper thumbnail of Sensor Data Interpretation in Bridge Monitoring-A Case Study

Frontiers in Built Environment, 2020

Large amount of data is obtained during bridge monitoring using sensors. Interpreting this data i... more Large amount of data is obtained during bridge monitoring using sensors. Interpreting this data in order to obtain useful information about the condition of the bridge is not straight forward. This paper describes a case study of a railway bridge in India and explains how multi-dimensional visualization tools were used to extract relevant information from data. Parallel axis plots were used to visually examine the data. Trends and patterns in data were observed, which were used for more detailed investigation. The case study shows the complexity in data interpretation even in the case of simple bridge configurations.

Research paper thumbnail of Development of automated top-down construction system for low-rise building structures

International journal of industrialized construction, 2020

Automation is the best solution for achieving high productivity and quality in the construction i... more Automation is the best solution for achieving high productivity and quality in the construction industry at reduced cost and time. The main objective of this study is to develop an economical automated construction system (ACS) for low-rise buildings. The incremental development of the construction system and the structural system through different versions of laboratory prototypes are described in this paper. These ACS prototypes adopt a top-down construction method. This method involves the building of the structural system step by step from the top floor to the bottom floor by connecting and lifting structural modules. ACS prototype 1 consist of wooden structural modules and electric motor system. ACS prototype 2 has a highly automated custom designed hydraulic motor system to construct steel structural frame. ACS prototype 3 is a partially automated system where the steel structural modules are connected manually. These prototypes were evaluated on the basis of function, cost and efficiency of operations. Based on overall performance, ACS prototype 3 is identified as the best economical option for the construction of low-rise buildings. When the speed of construction is more important than cost, the ACS prototype 2 is the apt solution. This paper describes the challenges in developing an ACS and the criteria to evaluate its performance. It also includes a preliminary framework for the development of an automated construction monitoring system and its experimental evaluation. This machine learning-based framework is to identify the operations of ACS from sensor measurements using Support Vector Machines. Most of the operations are identified reasonably well and the best identification accuracy is 96%. The future studies are focusing on to improve the accuracy of operation identification, further development of the monitoring system and the ACS for actual implementation in construction sites.

Research paper thumbnail of Form-Finding of Tensegrity Structures based on Force density method.pdf

Indian Journal of Science and Technology, 2016

Conference Presentations by Aparna Harichandran

Research paper thumbnail of Inferring Construction Activities from Structural Responses Using Support Vector Machines

35 thInternational Symposium on Automation and Robotics in Construction, 2018

On-site data collection during construction activities help in evaluating productivity rates and ... more On-site data collection during construction activities help in evaluating productivity rates and preparing more accurate schedules. One of the challenges here is in collecting data automatically such that activity start times and durations can be computed reliably. This paper proposes a methodology to infer construction activities that are being performed on site using the structural responses collected from construction equipments. This methodology is applied to the case of a launching girder, an equipment used in the construction of viaducts in metro rail projects. There are four stages involved in the construction of a viaduct; Auto launching, Segment lifting, Post tensioning and Span lowering. Strain values from the launching girder are used to predict the stages of construction using machine learning techniques. Support Vector Machines are used to classify the strain data into one of the four classes corresponding the stage of construction. Data from a typical construction cycle is used for training. Using the model generated by the training data, subsequent activities can be inferred.

Research paper thumbnail of A Robust Framework for Identifying Automated Construction Operations

37th International Symposium on Automation and Robotics in Construction, 2020

Machine learning techniques have been successfully implemented for the identification of various ... more Machine learning techniques have been successfully implemented for the identification of various construction activities using sensor data. However, there are very few studies on activity recognition in the automated construction of low-rise residential buildings. Automated construction is faster than conventional construction, with minimal human involvement. This requires high accuracy of identification for monitoring its operations. This paper discusses the development and testing of machine learning classifiers to identify normal automated construction operations with high precision. The framework developed in this work involves decomposing the activity recognition problem into a hierarchy of learning tasks in which activities at the lower levels have more details. The top recognition level divides the equipment states into two classes: 'Idle' and 'Operations'. The second recognition level divides the 'operations' into major classes depending on the top-level activities performed by the equipment. The third recognition level further divides the activities into subclasses and so on. Since the number of classes and the similarity between them increase with the recognition level, identification becomes extremely difficult. The identification framework developed in this study classifies operations belonging to the parent class at each level in the hierarchy. The efficacy of this framework is demonstrated with a case study of a top-down modular construction system. In this construction system, the modules of a structural frame are assembled and lifted starting with the top floor followed by the ones below. The accelerometer data collected during top-down construction is used to identify the construction operations. The proposed framework shows superior performance over conventional identification using a flat list of classes.

Research paper thumbnail of Determination of Automated Construction Operations from Sensor Data Using Machine Learning

4th International Conference on Civil and Building Engineering Informatics, 2019

Automated construction creates an intricate working environment involving workers and machines. T... more Automated construction creates an intricate working environment involving workers and machines. The added complexity of automated construction demands a rigorous monitoring system compared to conventional construction. The first stage of developing such a monitoring system is the identification of construction operations. This paper discusses a methodology for the identification of construction operations from sensor data. The methodology is illustrated using the case study of a coordinated lifting equipment implemented in a laboratory. The data is collected from a small scale structural frame consisting of steel modules in a controlled laboratory condition. The automated system follows a top-down construction method where the major construction operations are performed at the ground level and the structure is lifted upwards in stages. Strain and acceleration measurements were collected from the structure during construction. Each operation is associated with a unique pattern of measurements at each sensor location. The measurement data is used for analysis by support vector classification. Parameters like error penalty (C) and width of Gaussian kernel (σ) were varied to obtain the best prediction results. The results of the analysis show that the linear classification gives better results compared to the nonlinear classification for all operations except coordinated lifting. However, coordinated lifting is the best-predicted operation with an accuracy of 96%. Selection of optimal values of C and σ enhances the accuracy of classification. The features extracted from data seems to highly influence the learning of the algorithm and the performance of prediction. The results show the potential for using machine learning techniques for monitoring automated construction operations.

Research paper thumbnail of Identification of the Structural State in Automated Modular Construction

36th International Symposium on Automation and Robotics in Construction, 2019

Automated construction involves complex interactions between machines and humans. Unless all poss... more Automated construction involves complex interactions between machines and humans. Unless all possible scenarios involving construction and equipment are carefully evaluated, it may lead to failure of the structure or may cause severe accidents. Hence monitoring of automated construction is very important and sensors should be deployed for obtaining information about the actual state of the structure and the equipment. However, interpreting data from sensors is a great challenge. In this research, a methodology has been developed for monitoring in automated construction. The overall methodology involves a combination of traditional model-based system identification and machine learning techniques. The scope of this paper is limited to the machine learning module of the methodology. The efficacy of this approach is tested and evaluated using experiments involving the construction of a steel structural frame with one storey and one bay. The construction is carried out by a top-to-bottom method. During the construction of the frame, 99 base cases of normal operations are involved. 158 base cases of possible failures have been enumerated. Failure cases involve, for example, certain lifting platforms moving faster than others, improper connections of joints, etc. Strain gauges and accelerometers are installed on the structure and the data from these sensors are used to determine possible failure scenarios. Preliminary results indicate that machine learning has good potential for identifying activities and states in automated construction.

Research paper thumbnail of Relevance of deep sequence models for recognising automated construction activities: a case study on a low-rise construction system

Journal of Information Technology in Construction

Recognising activities of construction equipment is essential for monitoring productivity, constr... more Recognising activities of construction equipment is essential for monitoring productivity, construction progress, safety, and environmental impacts. While there have been many studies on activity recognition of earth excavation and moving equipment, activity identification of Automated Construction Systems (ACS) has been rarely attempted. Especially for low-rise ACS that offers energy-efficient, cost-effective solutions for urgent housing needs, and provides more affordable living options for a broader population. Deep learning methods have gained a lot of attention because of their ability to perform classification without manually extracting relevant features. This study evaluates the feasibility of deep sequence models for developing an activity recognition framework for low-rise automated construction equipment. Time series acceleration data was collected from the structure to identify major operation classes of an ACS. Long Short Term Memory Networks (LSTM) were applied for ide...

Research paper thumbnail of A Critical Review on Methods for the Assessment of Trainees' Performance in Virtual Reality-based Construction Safety Training

Research paper thumbnail of State of the art Technologies that Facilitate Transition of Built Environment into Circular Economy

Proceedings of the 40th International Symposium on Automation and Robotics in Construction

Research paper thumbnail of Automated Recognition of Hand Gestures for Crane Rigging using Data Gloves in Virtual Reality

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

Research paper thumbnail of A Critical Review on Methods for the Assessment of Trainees' Performance in Virtual Reality-based Construction Safety Training

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

Virtual Reality (VR) is one of the transformative technologies that aid construction safety train... more Virtual Reality (VR) is one of the transformative technologies that aid construction safety training. This study isa systematic review of literature on estimating the performance of the trainees during VR-based construction safety training. The critical analysis of the selected literature identifiedseven focus areasof research, often overlapping with oneor more areas.The focus areas include hazard recognition training, personalised feedback,training methodimprovement, the effect of VR training, the efficacy of VR training, safety behaviour analysis, and automated safety analysis. Most studies focus on either training with existing scenarios for improving hazard recognition or enhancing the training method with the latest technologies or supplementary devices. The possibilities of automated data collection during VR training need to be further explored for quantitativelyestimatingthe participant performance and analysis of close calls or safety incidents.

Research paper thumbnail of A Robust Framework for Identifying Automated Construction Operations

Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC), 2020

Machine learning techniques have been successfully implemented for the identification of various ... more Machine learning techniques have been successfully implemented for the identification of various construction activities using sensor data. However, there are very few studies on activity recognition in the automated construction of low-rise residential buildings. Automated construction is faster than conventional construction, with minimal human involvement. This requires high accuracy of identification for monitoring its operations. This paper discusses the development and testing of machine learning classifiers to identify normal automated construction operations with high precision. The framework developed in this work involves decomposing the activity recognition problem into a hierarchy of learning tasks in which activities at the lower levels have more details. The top recognition level divides the equipment states into two classes: 'Idle' and 'Operations'. The second recognition level divides the 'operations' into major classes depending on the top-level activities performed by the equipment. The third recognition level further divides the activities into subclasses and so on. Since the number of classes and the similarity between them increase with the recognition level, identification becomes extremely difficult. The identification framework developed in this study classifies operations belonging to the parent class at each level in the hierarchy. The efficacy of this framework is demonstrated with a case study of a top-down modular construction system. In this construction system, the modules of a structural frame are assembled and lifted starting with the top floor followed by the ones below. The accelerometer data collected during top-down construction is used to identify the construction operations. The proposed framework shows superior performance over conventional identification using a flat list of classes.

Research paper thumbnail of A Conceptual Framework for Construction Safety Training using Dynamic Virtual Reality Games and Digital Twins

Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC), 2021

Research paper thumbnail of Development of Automated Top-Down Construction System for Low-rise Building Structures

International Journal of Industrialized Construction, 2020

Automation is the best solution for achieving high productivity and quality in the construction i... more Automation is the best solution for achieving high productivity and quality in the construction industry at reduced cost and time. The main objective of this study is to develop an economical automated construction system (ACS) for low-rise buildings. The incremental development of the construction system and the structural system through different versions of laboratory prototypes are described in this paper. These ACS prototypes adopt a top-down construction method. This method involves the building of the structural system step by step from the top floor to the bottom floor by connecting and lifting structural modules. ACS prototype 1 consist of wooden structural modules and electric motor system. ACS prototype 2 has a highly automated custom designed hydraulic motor system to construct steel structural frame. ACS prototype 3 is a partially automated system where the steel structural modules are connected manually. These prototypes were evaluated on the basis of function, cost an...

Research paper thumbnail of Sensor Data Interpretation in Bridge Monitoring—A Case Study

Frontiers in Built Environment, 2020

Research paper thumbnail of Inferring Construction Activities from Structural Responses Using Support Vector Machines

Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC), 2018

Research paper thumbnail of Equipment activity recognition and early fault detection in automated construction through a hybrid machine learning framework

Computer-Aided Civil and Infrastructure Engineering

Research paper thumbnail of A Conceptual Framework for Construction Safety Training using Dynamic Virtual Reality Games and Digital Twins

38th International Symposium on Automation and Robotics in Construction (ISARC 2021), 2021

The construction industry is suffering from a high rate of accidents that significantly affect th... more The construction industry is suffering from a high rate of accidents that significantly affect the overall performance of projects. Compared to the conventional safety training methods, Virtual Reality (VR) games offer a more immersive and interactive learning experience for the participants. However, training scenarios in most of the existing VR games lack complex tasks and the realistic environment required for actual construction. This work proposes a conceptual framework for dynamically updating VR training games through information streaming from the digital twins. The training scenarios in VR games are automatically created from the project intent information, project status knowledge, safety regulations, and historical knowledge provided by the digital twins. Therefore, construction workers can be trained in realistic training environments with relevant tasks they soon afterwards pursue. Dynamic VR training is expected mutually beneficial for enhanced digital twins and period...

Research paper thumbnail of A hierarchical machine learning framework for the identification of automated construction

J. Inf. Technol. Constr., 2021

A robust monitoring system is essential for ensuring safety and reliability in automated construc... more A robust monitoring system is essential for ensuring safety and reliability in automated construction. Activity recognition is one of the critical tasks in automated monitoring. Existing studies in this area have not fully exploited the potential for enhancing the performance of machine learning algorithms using domain knowledge, especially in problem formulation. This paper presents a hierarchical machine learning framework for improving the accuracy of identification of Automated Construction System (ACS) operations. The proposed identification framework arranges the operations to be identified in the form of a hierarchy and uses multiple classifiers that are organized hierarchically for separating the operation classes. It is tested on a laboratory prototype of an ACS, which follows a top-down construction method. The ACS consists of a set of lightweight and portable machinery designed to automate the construction of the structural frame of low-rise buildings . Accelerometers wer...

Research paper thumbnail of Sensor Data Interpretation in Bridge Monitoring-A Case Study

Frontiers in Built Environment, 2020

Large amount of data is obtained during bridge monitoring using sensors. Interpreting this data i... more Large amount of data is obtained during bridge monitoring using sensors. Interpreting this data in order to obtain useful information about the condition of the bridge is not straight forward. This paper describes a case study of a railway bridge in India and explains how multi-dimensional visualization tools were used to extract relevant information from data. Parallel axis plots were used to visually examine the data. Trends and patterns in data were observed, which were used for more detailed investigation. The case study shows the complexity in data interpretation even in the case of simple bridge configurations.

Research paper thumbnail of Development of automated top-down construction system for low-rise building structures

International journal of industrialized construction, 2020

Automation is the best solution for achieving high productivity and quality in the construction i... more Automation is the best solution for achieving high productivity and quality in the construction industry at reduced cost and time. The main objective of this study is to develop an economical automated construction system (ACS) for low-rise buildings. The incremental development of the construction system and the structural system through different versions of laboratory prototypes are described in this paper. These ACS prototypes adopt a top-down construction method. This method involves the building of the structural system step by step from the top floor to the bottom floor by connecting and lifting structural modules. ACS prototype 1 consist of wooden structural modules and electric motor system. ACS prototype 2 has a highly automated custom designed hydraulic motor system to construct steel structural frame. ACS prototype 3 is a partially automated system where the steel structural modules are connected manually. These prototypes were evaluated on the basis of function, cost and efficiency of operations. Based on overall performance, ACS prototype 3 is identified as the best economical option for the construction of low-rise buildings. When the speed of construction is more important than cost, the ACS prototype 2 is the apt solution. This paper describes the challenges in developing an ACS and the criteria to evaluate its performance. It also includes a preliminary framework for the development of an automated construction monitoring system and its experimental evaluation. This machine learning-based framework is to identify the operations of ACS from sensor measurements using Support Vector Machines. Most of the operations are identified reasonably well and the best identification accuracy is 96%. The future studies are focusing on to improve the accuracy of operation identification, further development of the monitoring system and the ACS for actual implementation in construction sites.

Research paper thumbnail of Form-Finding of Tensegrity Structures based on Force density method.pdf

Indian Journal of Science and Technology, 2016

Research paper thumbnail of Inferring Construction Activities from Structural Responses Using Support Vector Machines

35 thInternational Symposium on Automation and Robotics in Construction, 2018

On-site data collection during construction activities help in evaluating productivity rates and ... more On-site data collection during construction activities help in evaluating productivity rates and preparing more accurate schedules. One of the challenges here is in collecting data automatically such that activity start times and durations can be computed reliably. This paper proposes a methodology to infer construction activities that are being performed on site using the structural responses collected from construction equipments. This methodology is applied to the case of a launching girder, an equipment used in the construction of viaducts in metro rail projects. There are four stages involved in the construction of a viaduct; Auto launching, Segment lifting, Post tensioning and Span lowering. Strain values from the launching girder are used to predict the stages of construction using machine learning techniques. Support Vector Machines are used to classify the strain data into one of the four classes corresponding the stage of construction. Data from a typical construction cycle is used for training. Using the model generated by the training data, subsequent activities can be inferred.

Research paper thumbnail of A Robust Framework for Identifying Automated Construction Operations

37th International Symposium on Automation and Robotics in Construction, 2020

Machine learning techniques have been successfully implemented for the identification of various ... more Machine learning techniques have been successfully implemented for the identification of various construction activities using sensor data. However, there are very few studies on activity recognition in the automated construction of low-rise residential buildings. Automated construction is faster than conventional construction, with minimal human involvement. This requires high accuracy of identification for monitoring its operations. This paper discusses the development and testing of machine learning classifiers to identify normal automated construction operations with high precision. The framework developed in this work involves decomposing the activity recognition problem into a hierarchy of learning tasks in which activities at the lower levels have more details. The top recognition level divides the equipment states into two classes: 'Idle' and 'Operations'. The second recognition level divides the 'operations' into major classes depending on the top-level activities performed by the equipment. The third recognition level further divides the activities into subclasses and so on. Since the number of classes and the similarity between them increase with the recognition level, identification becomes extremely difficult. The identification framework developed in this study classifies operations belonging to the parent class at each level in the hierarchy. The efficacy of this framework is demonstrated with a case study of a top-down modular construction system. In this construction system, the modules of a structural frame are assembled and lifted starting with the top floor followed by the ones below. The accelerometer data collected during top-down construction is used to identify the construction operations. The proposed framework shows superior performance over conventional identification using a flat list of classes.

Research paper thumbnail of Determination of Automated Construction Operations from Sensor Data Using Machine Learning

4th International Conference on Civil and Building Engineering Informatics, 2019

Automated construction creates an intricate working environment involving workers and machines. T... more Automated construction creates an intricate working environment involving workers and machines. The added complexity of automated construction demands a rigorous monitoring system compared to conventional construction. The first stage of developing such a monitoring system is the identification of construction operations. This paper discusses a methodology for the identification of construction operations from sensor data. The methodology is illustrated using the case study of a coordinated lifting equipment implemented in a laboratory. The data is collected from a small scale structural frame consisting of steel modules in a controlled laboratory condition. The automated system follows a top-down construction method where the major construction operations are performed at the ground level and the structure is lifted upwards in stages. Strain and acceleration measurements were collected from the structure during construction. Each operation is associated with a unique pattern of measurements at each sensor location. The measurement data is used for analysis by support vector classification. Parameters like error penalty (C) and width of Gaussian kernel (σ) were varied to obtain the best prediction results. The results of the analysis show that the linear classification gives better results compared to the nonlinear classification for all operations except coordinated lifting. However, coordinated lifting is the best-predicted operation with an accuracy of 96%. Selection of optimal values of C and σ enhances the accuracy of classification. The features extracted from data seems to highly influence the learning of the algorithm and the performance of prediction. The results show the potential for using machine learning techniques for monitoring automated construction operations.

Research paper thumbnail of Identification of the Structural State in Automated Modular Construction

36th International Symposium on Automation and Robotics in Construction, 2019

Automated construction involves complex interactions between machines and humans. Unless all poss... more Automated construction involves complex interactions between machines and humans. Unless all possible scenarios involving construction and equipment are carefully evaluated, it may lead to failure of the structure or may cause severe accidents. Hence monitoring of automated construction is very important and sensors should be deployed for obtaining information about the actual state of the structure and the equipment. However, interpreting data from sensors is a great challenge. In this research, a methodology has been developed for monitoring in automated construction. The overall methodology involves a combination of traditional model-based system identification and machine learning techniques. The scope of this paper is limited to the machine learning module of the methodology. The efficacy of this approach is tested and evaluated using experiments involving the construction of a steel structural frame with one storey and one bay. The construction is carried out by a top-to-bottom method. During the construction of the frame, 99 base cases of normal operations are involved. 158 base cases of possible failures have been enumerated. Failure cases involve, for example, certain lifting platforms moving faster than others, improper connections of joints, etc. Strain gauges and accelerometers are installed on the structure and the data from these sensors are used to determine possible failure scenarios. Preliminary results indicate that machine learning has good potential for identifying activities and states in automated construction.