Purpose: This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.Design/methodology/approach: The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.Findings: This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).Practical implications: This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.Originality/value: This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

The architecture, engineering, and construction (AEC) industry has seen a significant rise in the adoption of Building Information Modeling (BIM) in the last few years. BIM software have launched with numerous robust capabilities and features to satisfy the ever-demanding needs of the AEC industry. Various factors are associated with the selection of BIM software depending on a company’s requirements and constraints. BIM software selection is a daunting process as most AEC industries are unaware of the factors to consider when making this important decision. This study focuses on identifying the critical success factors (CSFs) and their interrelationship for efficient BIM software selection. For this research, a questionnaire was developed and disseminated in two stages in India, the United States of America (U.S.A.), Germany, and the United Kingdom (U.K.). In the first stage, a total of twenty-six identified CSFs were analyzed with the factor comparison method (FCM) to identify the top fifteen CSFs. Subsequently, the identified top fifteen CSFs were further assessed by implementing Fuzzy DEMATEL to categorize them into cause-and-effect groups based on respective influence strength, depicted with a causal diagram. Out of fifteen CSFs, five and ten factors were grouped into the cause group and effect group for BIM software selection, respectively. The most important factors were identified as software functionality, BIM adoption strategies and processes, interoperability, staff competencies, BIM standards and regional regulations. The outcome of this research can help BIM user companies improve their BIM software selection framework and decision-making process during purchasing software.

As-built progress of the constructed pavement should be monitored effectively to provide prompt project control. However, current pavement construction progress monitoring practices (e.g., data collection, processing, and analysis) are typically manual, time-consuming, tedious, and error-prone. To address this, this study proposes sensors mounted using a UGV-based methodology to develop a pavement layer change classifier measuring pavement construction progress automatically. Initially, data were collected using the UGV equipped with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope, and GPS sensor in a controlled environment by constructing various scenarios of pavement layer change. Subsequently, four Long Short-Term Memory network variants (LSTMs) (LSTM, BiLSTM, CNN-LSTM, and ConvLSTM) were implemented on collected sensor data combinations for developing pavement layer change classifiers. The authors conducted the experiment to select the best sensor combinations for feature detection of the layer change classifier model. Subsequently, individual performance measures of each class with learning curves and confusion matrices were generated using sensor combination data to find out the best algorithm among all implemented algorithms. The experimental result demonstrates the (a_z + g_x + D) sensor combination as the best feature detector with high-performance measures (accuracy, precision, recall, and F1 score). The result also confirms the ConvLSTM as the best algorithm with the highest overall accuracy of 97.88% with (a_z + g_x + D) sensor combination data. The high-performance measures with the proposed approach confirm the feasibility of detecting pavement layer changes in real pavement construction projects. This proposed approach can potentially improve the efficiency of road construction progress measurement. This research study is a stepping stone for automated road construction progress monitoring.

The residential construction sector is critical to economic stability and housing availability. Residential construction demands often fluctuate due to demographic, economic, social, or market condition variables. This study seeks to investigate the significance of these external variables and produce a predictive model for residential construction demand using ElasticNet regression. Adopting New Zealand as a case study and leveraging datasets from Statistics New Zealand, this research identifies key demographic, economic, and market factors influencing four building categories: retirement villages, apartments, multiunit developments, and standalone houses. The research results indicate that age groups, particularly the 20−39 and 65+ age groups, and economic indicators, such as the house price index and unemployment rates, have high prediction powers. The models showed high accuracy for some categories, with R² values exceeding 0.87 for retirement villages and 0.91 for multi-units. Challenges were encountered with standalone houses and apartments due to residual variance. The research findings highlight the importance of targeted urban planning and policy adjustments to satisfy the requirements of specific age groups, address housing affordability and demographic shifts, and cater to prevailing market conditions. This research provides practical insights and guidance for urban planners, public housing agencies, residential developers, and residential contractors while offering a robust methodological framework for predictive modelling in the construction sector.