Benny Raphael

Existing studies on automated construction equipment monitoring have focused mainly on activity recognition rather than fault detection. This paper proposes a novel equipment activity recognition and fault detection framework called hybrid unsupervised and supervised machine learning (HUS-ML). HUS-ML first identifies normal operations and known faulty conditions through supervised learning. Then, an anomaly detection algorithm is applied to spot any unseen faulty conditions. The framework is tested using acceleration measurements from a low-rise automated construction system prototype. HUS-ML outperformed the conventional machine learning approach in activity recognition and fault detection with an average F1 score of 86.6%. The conventional approach failed to detect unseen faulty operations. HUS-ML identified known faulty operations and unseen faulty operations with F1 scores of 98.11% and 76.19%, respectively. The generalizability of the framework is demonstrated by validating it on an independent benchmark dataset with good results.

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.

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 were deployed at critical locations on the structure. The acceleration data collected while operating the equipment were used to identify the operations through machine learning techniques. The performance of the proposed framework is compared with that of the conventional approach for equipment operation identification which involves a flat list of classes to be separated. The performance was comparable at the top level. However, the hierarchical framework outperformed the conventional one when fine levels of operations were identified. The versatility and noise tolerance of the hierarchical framework are also reported. Results demonstrate that the framework is robust, and it is feasible to identify the ACS operations precisely. Although the proposed framework is validated on a full-scale prototype of the ACS, the effects of strong ambient disturbances on actual construction sites have not been evaluated. This study will support the development of an automated monitoring system and assist the main operator to ensure safe operations. The high-level operation details collected for this purpose can also be utilised for project performance assessment and progress monitoring. The potential application of the proposed hierarchical framework in the operation recognition of conventional construction equipment is also outlined.

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 topdown 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.

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.