Journal of Construction Engineering and Management

Abstract

Construction industrialization (CI) has dramatically altered how architecture, engineering, and construction (AEC) companies design, manufacture, build, operate, and manage. Despite its growing relevance in both academia and companies in the last decades, the CI field has, however, been considered unclear, fragmented, and with a high level of misunderstandings. To address this knowledge gap through a systematic literature review, this paper provides a holistic and comprehensive conceptualization of the phenomenon and its theoretical basis for the global engineering and construction management community. In total, 102 articles from the last 11 years (2010–2021) from the Scopus and Web of Science databases were collected and analyzed. So, one primary contribution of this paper is the establishment of a definition for the CI concept. Another important contribution is the definition of a conceptual framework that introduces industrialization as a new way of operating the business, where five major dimensions, namely strategy, organization, business processes, supply chain planning, and resources, need to be aligned and managed simultaneously for competitive advantage. Furthermore, 10 design principles, five influencing factors, and 19 initiatives are presented to provide an integrated and holistic view of the concept. This article gives businesses a better knowledge of what industrialization in the construction industry actually means, enabling them to move forward with this transformation. Finally, the research provides a basis for improving future contributions in CI and adjacent management disciplines.

Practical Applications

CI has emerged as a dramatic change in the way construction companies operate. However, there is still much of uncertainty about what this transformation really means and what it encompasses. These are the main questions that, through a systematic literature review, this paper seeks to answer. As a first contribution, this paper presents a definition of CI. Furthermore, the paper also reinforces that industrialization is a new way of managing and producing where five major dimensions have to be handled simultaneously to achieve competitive advantage: strategy, organization, business processes, supply chain planning, and resources. Along with this contextualization, 10 design principles, five influencing factors, and 19 initiatives are also presented in a framework that assists in defining and representing the phenomenon. This article enables companies to take a step forward in this transformation by providing a greater understanding of what industrialization actually means in the construction sector. The contributions of this research are also targeted to managers and researchers with expertise in the fields of industrial engineering and management who want to contribute to bridge the gap between construction and management. Finally, this article also provides new guidelines for further contributions in the field of CI research.

Despite remarkable advances in the construction industry, it is still among the most hazardous industries; accidents occur in the construction industry with different severity levels. Construction accident data sets are available for analysis, but they face heterogeneity and class imbalance issues. Multitudinous complexities and uncertainties in construction projects result in heterogeneity; this leads to poor predictive performance of machine learning algorithms. Class imbalance issues arise because accidents occur at different severities with unequal distribution, producing biased prediction results. This study aimed to assess the impact of clustering on construction accident analysis when a data set is heterogeneous and imbalanced and to take a step toward making incidents more predictable. Accidents were predicted following four data preparation approaches: unmodified, balanced, clustered and clustered + balanced. The $k$-means clustering algorithm was adopted to split the data into homogenous clusters. Synthetic minority oversampling technique (SMOTE) and $k$-means SMOTE (KMSMOTE) were used to overcome the class imbalance issue. Five different supervised machine learning algorithms—classification and regression tree (CART), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB) and artificial neural network (ANN)—were employed for the prediction process. The results indicated that clustering significantly improved the predictive performance of the algorithms. The use of clustering along with oversampling was also the most appropriate approach to analyze accidents, providing more accurate and reliable predictions. The improvements resulting from applying the approach were about 33%, 23%, and 33% in terms of average precision, recall, and F1-score, respectively. Moreover, the ensemble learning classifiers used, RF and XGB, outperformed the other models. Ultimately, this research assisted safety professionals in predicting outcomes more accurately and in undertaking more appropriate safety measures.

Within the construction industry, there are firms dedicated to designing, executing, and restoring all types of buildings. To achieve their objectives, design offices form multidisciplinary project groups with designers from the same or other organizations. Design offices form small, unstable, and dynamic social networks dissolved at the end of each project. The flow of information through such informal organizations is decisive in their good performance. To estimate the ability of information to flow in construction organizations, social network analysis (SNA) has been used. However, SNA metrics are hardly useful for characterizing small design offices. Indeed, it is difficult to establish by SNA the quality of a small social network regarding its transmission of necessary information and performance during production processes. The purpose of this research was to figure out a metric to assess the quality of social networks in design offices based on the links’ distribution regarding their connectivity for information flow and related to their performance. Metrics and criteria for quality and configuration in small social networks were studied. The organizational structures of six architecture design firms were analyzed through three of their main social networks, and statistics were analyzed for those networks’ distribution of links and performance. As a result, relevant correlations were obtained between the proposed indicator and the efficiency in the design of the projects. Most members of the design team maintained between two and five communication links with their colleagues in the development of projects during project capture, project development, and postsale or modifications. Managers can use this information to oversee their work teams, improving the quality and configuration of social networks aiming to better performance.

The construction work classification code is a crucial index for consistently collecting unit cost data from the bill of quantities (BOQ). Appropriate classification codes assigned to detailed construction work item descriptions are required to verify project cost and for quality control. However, the codification system is complicated and time consuming for estimators to follow. This study proposes a framework to recognize the text of detailed construction work item descriptions in the BOQ and automatically assign the most similar work classification code. The automatic assignment algorithm was designed to score the similarity of the tokenized words in the work item description based on whether they contain the same word. The framework was experimented with using the national roadway BOQ; this BOQ is used for the procurement process in South Korea. It transformed work item descriptions from unstandardized BOQs into standardized data with more than 91.12% accuracy. The results of this study can be used as a prerequisite step for standardizing construction life cycle cost data and the automatic generation of BOQs.

Abstract

Safety in the construction industry has always been a focus of attention. Existing methods of detecting unsafe behavior of workers relied primarily on manual detection. Not only did it consume significant time and money, but it also inevitably produced omissions. Currently, automated techniques for detecting unsafe behaviors rely only on the unsafe factors of workers’ ontology to judge their behaviors, making it difficult to understand unsafe behaviors in complex scenes. To address the presented problems, this study proposed a method to automatically extract workers’ unsafe behaviors by combining information from complex scenes—an image captioning based on an attention mechanism. First, three different sets of image captioning models were constructed using convolutional neural network (CNN), which are widely used in AI. These models could extract key information from complex scenes was constructed. Then, two datasets dedicated to the construction domain were created for method validation. Finally, three sets of experiments were conducted by combining the datasets and the three different sets of models. The results showed that the method could detect the worker’s job type and output the interaction behavior between the worker and the target (unsafe behavior) based on the environmental information in the construction images. We introduced environmental information into the determination of workers’ unsafe behaviors for the first time and not only output the worker’s job type but also determine the worker’s behavior. This allows the model output to be better for ergonomic analysis.

Practical Applications

This study developed an intelligent solution for determining whether a worker had unsafe behavior in complex scenarios using behavioral norms. The operator would not need to prepare the appropriate construction safety knowledge, such as whether to wear a helmet, whether to wear a safety belt, or whether to work at height, but simply input the target image into the model, and the model would combine the predefined behavioral norms, scene information, and other factors to determine what kind of behavior (or unsafe behavior) was contained in the image and output a simple description of the information. Descriptions could also be set as fixed templates for easy management, such as worker A wearing (not) a helmet, and these descriptions would play a key role in daily management and project summaries. Using this method, managers could use the relevant equipment to automate the acquisition of possible good behaviors or violations of anyone on site. It also enables efficient organization and recording, improving the efficiency of managers.

Making an optimal dredger fleet scheduling (DFS) plan is difficult for dredging works in existing ports, where the dredging activities are interrupted by stochastic vessel traffic. Therefore, a simulation optimization (SO) approach is adopted to solve the DFS problem. In the SO approach, an agent-based simulation model is first developed to simulate the stochastic traffic flow and possible spatiotemporal traffic conflicts between vessels and dredgers. Then, this simulation model is integrated with an optimization model to find the optimal DFS plan with the objective of minimum total dredging equipment usage fees. Finally, the proposed SO approach is applied to a 4-dredging-job dredging work. The results demonstrate its applicability in identifying the bottlenecks of the DFS plan and finding the optimal DFS plan. This research contributes to gaining more insights into using the SO approach to provide decision making for dredging work planning under uncertainty.

Women’s representation in construction and engineering continues to be low. Drawing on the stewardship theory, we investigated how women in the top management team and women in management drive the appointment and promotion of women at management and nonmanagement levels, respectively. We used a time lag research design and archival data from 495 construction and engineering organizations operating in Australia. The results of structural equation modeling suggest positive relationships between women’s representation in management and both the appointments and promotions of women in nonmanagement roles. The findings also suggest some unpredicted relationships: women’s representation in management is linked to the appointments of women managers, and hiring more female managers is linked to the promotion of more female managers. Together, these findings suggest that female managers act as “stewards” in their individual capacity.

Crane operators and signalmen play an integral role in the safe and efficient operation of cranes on a construction site. Operating a crane is a complex and demanding task that requires careful coordination between operator and signalmen in order to avoid errors that could have dire consequences, including serious injury or loss of life. Therefore, special considerations should be taken to mitigate communication errors that could occur between the two parties. Technology can play an important role in enhancing communication, and, with recent advancements in technology, human–computer interaction has emerged as an active area of research within the field of computer vision. This paper presents a framework that integrates the YOLOv4 model (for object detection) and the long short-term memory (LSTM) model (a recurrent neural network) for dynamic hand signal classification in real time. The first step is the creation of a crane signalman dynamic hand signal data set with 18 classes. The YOLOv4 model is then customized for this application by modifying the activation function. Three modified YOLOv4 models are then integrated with the LSTM model. The modified YOLOv4 integrated with LSTM is found to achieve a maximum overall accuracy of 94.8% with an inference time of 55.1 frames per second. The model is further validated with real-time dynamic hand signal classification, achieving an accuracy of 93.5% and an inference time of 44 frames per second. The proposed models show improved quality in classification accuracy as well as in processing speed in comparison to some of the most widely used models currently in use. The proposed novel framework can be used as another layer of communication to supplement current practice and reduce communication errors between crane signalmen and crane operators.

Labor costs constitute a significant portion of construction costs. Reliable forecasts provide insight into the movements of labor costs and are critical to the success of projects. Past studies have primarily focused on forecasting construction cost indexes or material costs. Only a few studies have concentrated on forecasting construction labor costs. This study presents a multivariate Bayesian structural time series (MBSTS) model to characterize the future values of construction labor’s average hourly earnings (AHE) using a set of candidate predictors. The methodologies commonly used by past studies do not adequately address the uncertainties associated with the modeling process. In contrast, MBSTS recognizes the uncertainty in its modeling process, which enables practitioners to quantify and account for future labor cost risks in decisions. Furthermore, the MBSTS method results in a transparent model, helping analysts investigate the rationality of the parameters. This article trains MBSTS models under four different data subset lengths (i.e., 150, 144, 138, and 132 months) to study the consistency of the explanatory variables and their corresponding coefficients. The analysis results indicate that the gross domestic product (GDP), housing starts (HS), number of building permits (BP), Construction Cost Index (CCI), Dow Jones Industrial Average (DJI), and Standard and Poor’s 500 index (SPI) are the most frequently used predictors in the regression component of the MBSTS models. The results indicate an inverse relationship between AHE from one side and HS and BP from the other. Likewise, a direct relationship exists between AHE and GDP, CCI, DJI, and SPI. The MBSTS model performed well on the validation subset in the midrange prediction intervals (i.e., 12- and 18-month periods). However, it was outperformed by conventional time series models [i.e., seasonal autoregressive integrated moving average (SARIMA)] when used for short-term forecasting. The proposed framework can be applied to facilitate monetary resource allocation in projects.

To promote precision installation accuracy of engineering structural components and improve construction quality, point cloud data obtained by 3D laser scanning are widely used in construction quality monitoring. Reasonable parameters are very important to control errors and improve efficiency during the process of 3D laser scanning. Therefore, this paper proposes a random forest-nondominated sorting genetic algorithm (RF-NSGA-II) multiobjective optimization model with an elite strategy. To study the relationship between 3D laser scanning parameters and measurement errors or efficiency, the best scanning parameters are identified. In this paper, high-precision prediction of the relative error and scanning time by 3D laser scanning parameters is achieved by using an RF, and the nonlinear mapping relationship function is obtained, which is used as the objective optimization function. The RF-NSGA-II multiobjective optimization algorithm is developed to optimize the relative error and scanning time, and the scanning time is the shortest under the condition of reasonable relative error. Through this study, we can mainly draw the following conclusions: (1) based on the RF, we can obtain a prediction model of relative error and scanning time with high accuracy, in which the R² (determination coefficient) value and root mean square error (RMSE) of the relative error prediction model are 0.967 and 0.0277, respectively, and the R² value and RMSE of the scanning time prediction model are 0.978 and 0.0243, respectively; (2) optimized design parameters for 3D laser scanning of structural components are obtained, including a horizontal incident angle of 90°, an inclination angle of 90°, point cloud density of 3.2 mm, measurement distance of 3 m, resolution of 0.456, and visibility of 8.67 km; and (3) the RF-NSGA-II model developed can effectively reduce the relative error of the 3D laser (by 2.16 mm) and shorten the scanning time (by 148.926 s) compared with the average value. The structural components also meet the requirements in the deviation test. Therefore, the application of the RF-NSGA-II model in the assembly and fabrication of structural components can realize the intellectualization of the production process and improve the precision of the prefabrication of structural components, which has high engineering application value.

Innovation is widely considered the lifeblood of organizational survival and growth. However, despite its importance, innovation diffusion does not take place spontaneously in organizations. In fact, diffusion of innovations (DoI) in project-based organizations (PBOs) is widely considered notoriously lukewarm. Nevertheless, it is inspiring to observe that project teams could suddenly change their lukewarm attitude in DoI when they receive pressure from the authority and industrial associations, or when they notice their peers starting to adopt an innovation. There seem to be some isomorphic pressures such as coercive, mimetic, and normative pressures that drive DoI. This research aims to identify the source factors of the isomorphic pressures that can be used to catalyze innovation diffusion with a focus on construction PBOs. It does so by conducting a 3-year in-depth case study in an international construction firm using a mixed-method approach including semiconstructed interviews and nonparticipatory observations. The research identified 12 specific source factors under the three categories of isomorphic pressure, i.e., coercive pressure, mimetic pressure, and normative pressure. Coercive pressure can arise from organizational policy, company culture, and influence from seniors; memetic pressure can stem from the influence of peer project teams as well as change agents such as innovation champions; and normative pressure derives from the sharing in regular events/meetings and related training. The practical contribution is that based on the source factors identified, top managers can tactically create such pressures to catalyze DoI meanwhile avoiding excessive pressures that might hinder DoI in their companies. Future studies are recommended to unravel the mechanism through which these source factors interact to take an effect, and based on that, devise the strategies that can harness the isomorphic pressures to boost DoI.

Due to the great potential but limited use of building information modeling (BIM) as a milestone technology to reshape traditional design and construction practices, governments in many regions of the world have formulated specific sets of complementary policy instruments, namely, policy mixes, to facilitate the development of BIM in the construction industry. Drawing on institutional theory and the organizational sensemaking literature, this study aims to examine how regional institutional policies impact organizational BIM implementation practices and how these impacts are moderated by different sources of uncertainty related to policy making and enforcement from a policy mix comprehensiveness perspective. The effects were empirically tested with a panel data set of 992 high-tech construction firms from 31 provincial jurisdictions in China during 2011–2021. The results provide clear evidence that while regional policy mix comprehensiveness positively impacts the level of BIM implementation at the organizational level, this relationship becomes stronger when firms are experiencing higher levels of environmental state uncertainty derived from regional politician turnover but weaker when firms are experiencing higher levels of response uncertainty derived from organizational top management team (TMT) turnover. As an exploratory effort to empirically assess the impacts of policy environments in the conservative construction industry through integrating the complexity of both regional policy instruments and organizational decision-making processes, this study contributes to a deepened understanding of how construction firms interact with external environments to implement innovative technologies under institutional pressures. The findings reinforce the need to devise policies for innovation diffusion in the construction industry from a more systemic and contingent perspective.

In industrial modular construction, components fabricated in the shop are assembled into modules before being transported to construction sites. This approach offers improved construction efficiency, higher quality products, and the opportunity to work in a controlled environment that offers more predictable schedules. A distinct category of modularized construction is that of piping structures where prefabricated segments of pipes, called pipe spools, are arranged and attached to a steel structure to speed up on-site assembly. Determining when a particular pipe spool is ready for delivery to the site is affected by many factors, including material availability, workforce performance and availability, shop loading conditions, and shop capacity. These factors combine to produce a high degree of uncertainty when estimating the fabrication duration. This paper investigates the improvement of delivery time prediction of pipe spools that are fabricated in a manufacturing facility. The study’s main contribution is to propose a data-driven machine-learning model that accurately predicts the total fabrication duration in a pipe spool fabrication facility. The developed model is expected to assist production managers to plan for the appropriate workforce and material delivery requirements. The proposed approach utilizes a combination of product composition and real-time loading status of the shop to develop and examine several tree-based machine-learning models. The approach was evaluated using records of 6,989 pipe spools. The overall prediction performance was improved by a reduction of 35% in the mean absolute percentage error compared to original heuristic estimates. The results also show a strong positive correlation of 0.8 between the actual and predicted values.

Abstract

Ethnic minority workers (EMWs) or migrant workers continue to be confronted with communication problems, which can have serious effects on safety outcomes. This study aims to help improve construction industry EMW safety communication by uncovering and analyzing the criticality, underlying constructs, and explanatory power of the influencing factors involved. A mixed research design was employed by combining a literature review, semistructured interviews, and a questionnaire survey. Following the identification of an initial set of safety communication factors (SCFs) and a questionnaire designed and administered to EMWs in the Hong Kong and Australian construction industry, a total of 134 valid responses were analyzed through mean scoring, factor analysis, and importance-explanation analysis. Eighteen critical EMW SCFs are identified and categorized into three groups of worker-related SCFs, manager-related SCFs, and organization-related SCFs. A set of 36 SCFs identified cannot only be used to examine the relative importance of EMW SCFs, but can also be adopted to capture the critical SCFs for both local and ethnic minority construction workers in other countries. Narrowing a wide range of SCFs for EMWs provides stakeholders with the insights needed to the key contributory factors of safety communication, which in turn has a positive impact on safety performance. Despite this study being conducted in Hong Kong and Australia, its findings can also be used as a reference for other countries where EMWs are employed (e.g., the US, the UK, Canada, the Middle East, Malaysia, Singapore, and South Africa).

Practical Applications

Safety communication is a major safety challenge for EMWs and effective safety communication leads to improved safety performance and decreased injuries and fatalities. This research provides extra information on the key issues of safety communication that associated stakeholders need to address for EMWs to help them understand and mitigate the main safety communication barriers. This study suggests that apart from some measures (e.g., language courses, understandable safety training and safety materials, and bilingual translators) that have been taken for improving EMW safety communication in many countries, governments and employers are recommended to adopt multifaceted strategies manipulating worker-, manager-, and organization-related SCFs that would be more effective than a single measure. The critical roles of managers and organizations in promoting EMW safety communication are also emphasized in this study. Future efforts to improve or develop programs or interventions for EMW safety communication can benefit from this study by referring to the critical SCFs to include each aspect of safety communication. Furthermore, the identified critical SCFs will also help industry practitioners diagnose deficiencies in EMW safety management practices.

Project risk is an important part of managing large projects of any sort. This study contributes to the state of knowledge in project risk management by introducing a data-driven approach to measure risk identification performance using historical data. In the early phases of a project, the identification and assessment of risk is based largely on experience and expert judgment. As a project moves through its life cycle, these identified risks and the assessment of them evolve. Some risks become issues, some are mitigated, and some are retired as no longer important. This study investigated the quality of early risk registers and risk assessments on large transportation projects and compared them to how the identified risks evolved on historical projects. The investigation involved the use of textual analysis of archival risk register documents. Finite-state automation methods akin to Markov chain models were used to track the changes in risk attributes on large infrastructure projects as the projects matured. The objective was to be better able to anticipate how project risks will change as projects move forward and to be better able to forecast changes to the risk register from ex ante to ex post conditions. Results from 11 major US transportation projects suggested that, on average, fewer than 65% of ex ante identified risks ultimately occurred in projects and were mitigated, while more than 35% did not occur and were retired. In addition, more than half of the risks emerged during project execution when new information became available. Based on the categorization of risk management styles, we find that identifying risks early in the project life cycle is necessary, but not sufficient to ensure successful project delivery. A project team with positive doer behavior (i.e., actively monitoring and identifying risks during project execution) performed better in delivering projects on time and within budget.

Every year more than 100 fatal accidents occur in road work zones. One of the major causes of pedestrian workers being struck by construction vehicles is that workers become habituated to the warning alarms of these vehicles. Researchers suggest that workers with certain personality traits (e.g., boredom proneness and extraversion) are more likely to become habituated to workplace hazards and therefore have a higher likelihood than other workers of being involved in an accident. This study investigated which aspects of personality correlate with workers’ accident proneness and their vulnerability to habituation to warning alarms in road work zones. An experiment with actual road construction workers was performed using a virtual reality (VR) environment. The results reveal that boredom susceptibility (one of the subdimensions of the personality trait of sensation seeking) is negatively correlated with workers’ attention to warning alarms, and that boredom-prone workers were more likely to be involved in a virtual struck-by accident. The findings of this study provide conceptual motivation for tailoring safety training to individual workers’ personality traits.

Given the importance of accurate contingency estimation to properly absorb cost fluctuations, there have been enormous efforts to identify risk factors and measure contingency in roadway projects. However, no study has proposed a contingency framework that reflects the linkage effects of factors through the project phases. To this end, this research develops a project phase-based contingency adjustment framework using a path analysis drawing on a dataset of 1,053 rehabilitation, reconstruction, and resurfacing (3R) roadway projects in Florida. In particular, our framework incorporates the sequential effects of factors through the lenses of project phases from planning and bidding and contract to construction and completion. The developed path model shows that individual factors may have direct and/or indirect impacts on project costs. The results of this study can be applied to better contingency and cost estimation and proper risk management in the preconstruction phases of roadway renewal projects.

Construction projects are dynamic by nature because of continuously moving resources such as heavy equipment and workers. This nature necessarily results in proximity hazards, especially on a large-scale construction site. Especially, struck-by accidents still account for about 38% of the total injuries in the US construction industry. Although there have been several efforts to mitigate the hazards, the statistics show that the hazards still persist. To provide a practical solution to this problem, this study proposes an Internet of Things (IoT)-based proximity warning system that provides an alert to workers whenever they are close to heavy equipment. The system includes equipment protection units (EPUs), personal protection units (PPUs), and Bluetooth low energy (BLE) beacons. A framework of signal processing and alert logic was developed to estimate the distance between PPUs and EPUs and to activate alerting modules timely. By calculating the distance based on the signal strength using a particle filtering method, EPUs and PPUs provide auditory and vibration alerts to equipment operators and workers when they are in an alert range. Also, practical alert logic was developed based on the site worker’s feedback. This study validates the system performance with different signal processing methods and alert logic through five real-world field tests. The system achieved a precision, recall, and F1-score of 89.21%, 97.45%, and 0.931 from the field tests, respectively. Also, positive feedback was obtained from the participating workers. The proposed IoT-based proximity warning system has a high potential for a practical solution to proximity hazards in actual construction sites.

During negotiations in the initial stage of multinational megaprojects, technical issues are underemphasized compared to diplomatic or political perspectives despite varying relevant standards of participating countries. If the organization structures are established soundly while overcoming heterogeneous aspects of involved countries, decision making among countries satisfying both could be made timely and properly. With the aim of positive multilateral relationships and negotiations concerning engineering factors collectively, a framework is presented for identifying the roles and responsibilities of multinational joint clients with a hierarchical organization structure composed of a decision maker, senior responsible owner, project sponsor, and project manager. US forces relocation program in the Republic of Korea, a $1.4 billion megaproject, was analyzed where political and cultural aspects were attempted to melt down in a hierarchical organization. Results from interviewing high-level decision makers from both countries show that upper-level participants focus on political issues, while lower-level practitioners aim to achieve satisfactory technical quality. The involvement of engineers in earlier negotiation stages with upper-level counterparts is essential in establishing realistic goals. The uniqueness of the framework compared to previous factor-driven perspectives between different joint-client structures are as follows: Identical hierarchy of level from the countries supports defining roles with integrated scope management, and neutral consultative body for arbitrating conflict between countries and in-house departments within a country is required.

The construction industry has been suffering from delays and cost overruns for decades. Experienced schedulers programs and allocate contingencies (both cost and time) based on professional experiences and gained knowledge. Such tacit knowledge has not been captured, stored, and shared with inexperienced schedulers. This paper proposes a graph-based automated scheduling (GAS) method to capture, store, and reuse the tacit knowledge in the construction schedules. The proposed GAS method takes construction schedules as input, extracts schedule features, classifies schedules into different types of sequences, selects and assembles sequences into schedules, and eventually optimizes time- and cost-efficiency of assembled schedules. The GAS method was validated on two case studies. The results indicated that the automatically generated construction schedule is, on average, 6.70% closer to the actual schedule than the planned schedule. Different from existing automatic scheduling methods, GAS relies little on the availability and data richness of building information modeling (BIM) models. Hence, GAS helps schedulers initiate new schedules more efficiently at the early construction stages.