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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.

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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.

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.