Journal of Construction Engineering and Management

Although social control is the preferable control strategy in projects with low-level outcome measurability and behavior observability, empirical evidence for its effectiveness in such cases is sparse. To fill the knowledge gap, this study explores whether social control is effective in the alleged applicable contexts, taking outsourced architectural and engineering (A/E) design consulting projects as the empirical setting. A questionnaire survey was conducted to collect data. The results show that social control fosters designers’ in-role and extra-role behaviors directly or indirectly through enhancing their autonomous motivation. High-level formal controls may weaken the positive effects of social control on design behaviors. This study contributes to the body of knowledge by adding a social-psychological perspective to organizational control research and by providing empirical evidence for the effectiveness of social control in outsourced A/E design consulting projects. It also offers managerial implications in properly arranging control strategies in outsourced A/E design consulting projects.

Scope of work requirements (SOWRs) specify the contractual obligations for the contractor to fulfill. Managing the information is crucial to the success of a construction project. This process is, however, often challenging because requirements are typically voluminous and written in an unstructured paper-based format. The current state-of-the-art models are mostly applicable to quantitative constraints for use in automated code compliance checking of design features. This study proposes a novel natural language processing (NLP) framework capable of digitalizing nonquantitative natural language SOWRs outlined in construction contracts. The model includes a comprehensive set of semantic and syntactic rules of linguistic features for extracting contractual work information including actors, actions, objects, constraints, tasks, and obligation. The framework was evaluated on a large textual corpus of provisions of design-build highway contracts. The model yields an impressive precision and recall of more than 93% and 87%, respectively. The proposed system is expected to help the project planner quickly develop an electronic database of contractual work requirements, enabling significantly improved efficiency in project planning and verification.

Clay bricks are a popular building material, especially in historical structures. Understanding the chemical and mechanical properties of bricks is vital in historical structure rehabilitation attempts, because discrepancies in the material could lead to accelerated degradation and structural failure. This research focuses on understanding the mechanical and chemical properties of clay bricks used in the world heritage city of Kandy, Sri Lanka, over the chronological period of the 18th to the 21st centuries. Chronological analysis of brick properties reveals that optimal mechanical properties in bricks are closely related to the water absorption rate and are found to be between 10% and 18% in the analyzed specimens. Further, regional mineralogical properties, such as high concentration of calcium and inclusion of barium (Ba), potassium (K), strontium (Sr), and titanium (Ti) as trace elements, were visible in most samples regardless of the year the brick was produced. The analysis also identified revealed that the inclusion of ferrous (Fe) increases performance. Assuring similar chemical characteristics in restoration material is important to avoid chemical imbalances leading to accelerated deterioration due to the presence of reactive metals in the brick composition.

At state transportation agencies (STAs), construction inspection is pivotal to ensuring infrastructure quality. Due to current challenges, STAs have been exploring a risk-based inspection strategy. However, the lack of a systematic approach for risk identification and assessment at the construction activity level is the main limitation. Therefore, this paper introduces an integrated risk-based construction inspection framework by developing a risk breakdown matrix (RBM). Using RBM, critical risks are identified and assessed at the construction activity level. The overall framework is evaluated by using the concrete pavement project as a test case. Results show that the framework could provide risk information (e.g., risk rankings) of construction activities as well as the overall project risk level. The major contribution is that the proposed framework can be applied to various infrastructure projects at STAs, allowing for effective inspection resource allocation, reduction of the inspector’s workload, and consistency in inspection practices.

The construction industry in many developing countries is considered the main engine for economic growth. Quantification of the resilience of the construction industry in developing economies is essential for stakeholders and decision makers. Many researchers have attempted to quantify the construction industry’s resilience in the context of developed economies; however, there is lack of established measures of such quantification in developing countries. This paper proposes a framework for the composition of an index that quantifies the resilience of the construction industry in developing countries. The proposed framework is demonstrated in the context of three developing countries: Rwanda, Egypt, and Turkey. The index is composed of measures such as the construction value added to a country’s gross domestic product (GDP) and employment in construction. Principal Component Analysis (PCA) is utilized for weighting and aggregation of the individual variables. Studying the causal relationship between construction growth and economic development from 1971 to 2022, results show that construction growth leads to economic development in each of the three countries. Results of the proposed index values indicate that the construction industry in each of the three countries demonstrated increased resilience by sustaining both its outputs and its employment generation aspect in the two years following the coronavirus pandemic in 2019. Quantification of the construction industry’s resilience in countries where the construction growth leads to the economic growth would provide a crucial insight for stakeholders and decision makers.

Abstract

Despite a recent surge in publications on construction safety, the findings on the effect sizes of antecedents of safety behavior are still dispersed and equivocal. This research aimed to analyze the influences of diverse antecedents on safety behavior by accumulating the effect sizes through a meta-analysis of existing studies on the high-risk construction industry. Data were gleaned from 58 primary articles to calculate effect sizes for 21 antecedents. The results quantified effects among three categories of safety behavior antecedents: proximal personal factors (i.e., safety knowledge, motivation, and strain), distal situational factors (i.e., job demands and job resources), and distal personal factors (i.e., key resources and job attitudes). We also exploratively showed that safety behavior antecedent effect sizes were stronger in cultures with high uncertainty avoidance and varied inconsistently when safety behavior was measured with an industry-specific scale compared with a universal one. An exemplar path model verified that the three factors, namely safety knowledge, motivation, and strain, partially mediated the effects of situational antecedents (job demands and job resources) on safety behavior. Through a cumulative assessment, this research contributes to the construction safety literature by quantifying the relationship between safety behavior and its antecedents, emphasizing the fundamental significance of contingent factors and confirming a synthesized conceptual model that informs the mechanisms liking safety behavior and its antecedents.

Practical Applications

Workplace safety is a critical issue in the construction industry. Failure to effectively intervene in the unsafe behavior of construction workers will result in serious consequences such as accidents and casualties. Previous studies have mostly uncovered behavioral antecedents from a single perspective. However, in the process of management practice, safety behavior is not the result of a single factor, which makes it necessary to effectively identify factors as comprehensively as possible. Using meta-analysis, this study summarizes 21 antecedents in three categories: proximal personal factors (i.e., safety knowledge, motivation, and strain), distal situational factors (i.e., job demands and job resources), and distal personal factors (i.e., key resources and job attitudes). The effect sizes of these 21 antecedents with safety behavior were also calculated, which may help the management make decisions on safety behavior interventions when resources are constrained. Furthermore, the cultural background of construction workers also needs to be considered in the implementation of interventions. When there is a high level of fear of uncertainty in a culture, the strain of construction workers can force them to act more unsafely. Finally, it should be stressed that the antecedents are not isolated: job demands and job resources will influence safety behavior through safety knowledge, motivation, and strain.

Occupational accidents are frequent in the construction industry, containing significant risks in the working environment. Therefore, early designation, taking preventive actions, and developing a proactive safety risk management plan are of paramount significance in managing safety issues in the construction industry. This study aims to develop a national data-driven safety management framework based on accident outcome prediction, which helps anatomize precursors of fatalities and thereby minimizing fatal accidents on construction sites. A national data set comprising 338,173 occupational accidents recorded in the construction industry across Turkey was used to develop a data-driven model. The random forest algorithm coupled with particle swarm optimization was used for the prediction and the interpretability of the proposed model was augmented through the game theory–based Shapley additive explanations (SHAP) approach. The findings showed that the proposed algorithm achieved satisfactory model performances for detecting construction workers who might face a fatality risk. The SHAP analysis results indicated that both company (such as number of past accidents and workers in the company) and worker-related (such as age, daily wage, experience, shift, and past accident of the workers) attributes were influential in identifying fatalities by detecting which workers might face fatal accidents under which conditions. A construction safety management plan was developed based on the analysis results, which can be used on construction sites to detect workers/conditions that are most susceptible to fatalities. The findings of the present research are expected to contribute to orchestrating effective safety management practices in construction sites by characterizing the root causes of severe accidents.

Construction delays are among the industry’s most significant challenges, especially in the infrastructure sector, where delays can have serious socio-economic consequences. Recently, advances in deep learning (DL) have opened up new possibilities for tackling complex issues more efficiently. This study aims to evaluate the potential of deep neural networks in predicting the level of delay in Iranian dam construction projects. As the first step, 65 delay risk factors were identified through a comprehensive literature review and interviews. Then risk scores for 53 completed dam projects in Iran were determined through a questionnaire survey. Subsequently, the most significant latent features were extracted using principal component analysis (PCA). The resultant variables were combined with two project characteristics to develop the input dataset. Finally, the resulting dataset was used to develop a deep multilayer perceptron neural network (MLP-NN) model to predict project delays. The prediction performance of the deep-MLP model was then evaluated and compared to that of the best delay prediction models found in previous studies. The three-times repeated stratified five-fold cross-validation results demonstrated that the proposed deep-NN model outperformed all previous approaches for delay prediction on all performance metrics. This study also explores the effectiveness of combining delay risk factors with project characteristics to train the predictive model. According to the results, adding project characteristic factors to the training dataset significantly improved the prediction performance of deep-MLP. The work presented here can assist managers of future dam constructions in the early stages of the project in selecting and prioritizing projects within a portfolio and allocating a sufficient buffer to ensure the project’s timely completion.

Less polluting and more efficient projects can be developed when considering sustainability and constructability in the initial phases of building design. The building information modeling (BIM) methodology uses 3D graphical modeling and parametrized objects and, thus, provides an environment that can seamlessly assess these factors. An in-depth systematic literature review revealed that when these issues are addressed using BIM, they are not considered within the same framework. To address this gap in knowledge, the present work proposes a BIM-based workflow that involves the different agents in a construction, in order to promote a greater participation of builders and environmental specialists at the initial stages of the design. In addition, a plugin is developed in the Autodesk Revit software, named ECOS, that quantifies the constructability and sustainability of a project and identifies which aspects of the project are deficient within this context. ECOS also provides a cost criterion, as this is also an important aspect when evaluating a project. The proposed workflow and the plugin are tested and validated through a survey in conjunction with interviews of sixteen architectural and engineering professionals. The results show that the proposed process map associated with ECOS form a useful framework to promote greater participation of builders and environmental engineers in the initial stages, and, thus, can promote more sustainable designs with better constructability.

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects.

Although a great deal of worker safety risks analysis has been conducted, safety accidents continue to occur and recur at regular intervals. On construction sites, various activities have different levels of accident probability and severity, and existing methods are limited because they involve the use of the same weights to assess accident probability and accident severity. In addition, uncertainties that occur over time result in rapidly changing risks. The majority of existing approaches provide postevent warnings, so workers may not have sufficient time to prevent accidents once they are warned about the possibility. Thus, there is a need for mechanisms that can be used to assess safety risk regularity, predict worker risk levels, and provide proactive warnings based on the comprehensive consideration of the impacts of worker behaviors and environments. To address these issues, a safety prediction model was proposed for use, and an active warning mechanism was constructed for construction workers. The prediction model performs accident potential regularity analysis based on attribute-based safety risk analysis and precursor analysis. Further, it quantifies worker risk levels using decision matrix risk assessment (DMRA) and the gray absolute decision analysis (GADA) method. The model overcomes the limitation of using the same weights in DMRA to assess accident probability and severity. An active warning mechanism for construction workers was created to validate the efficacy of the safety prediction model, and the proposed safety prediction model is embedded in the mechanism. The mechanism mainly consists of three modules: (1) a data collection module that mainly includes expert knowledge and dynamic safety information from surveillance cameras; (2) a data analysis module that mainly uses the proposed safety prediction model to predict individual worker risk levels; and (3) an early warning module that displays the predicted risk levels, dynamically ranks risk indicators, and provides corresponding early warning measures. Finally, the feasibility and operability of the proposed active warning mechanism are demonstrated through a practical case study.

Fall from height (FFH) is the most common construction accident in the construction industry, thus it is significant to monitor the use of safety harnesses, which are critical to the prevention of FFH. Sensing or computer vision technologies have been adopted to identify workers’ safety harness usage. However, previous research focused mainly on whether a worker wears a safety harness rather than on whether he or she properly fixes it to a lifeline, which is vital to prevent FFH but difficult to monitor. This research establishes an action recognition method based on a three-dimensional (3D) skeleton and long short-term memory (LSTM) to aid in automatically monitoring whether safety harnesses are fixed properly on site. An indoor experiment, which considered the features of a common real construction scenario—working on scaffolding—was conducted to test the effectiveness and feasibility of the proposed method. The result shows that the method achieves an acceptable precision and recall rate and can be used to detect the incorrect use of safety harnesses by combining multiple actions. This will contribute to the prevention of FFH in practice as well as to the body of knowledge of construction safety management.

Within the context of sustainable innovation in the building industry, the goal of this study is to investigate and discuss forms of cooperative learning in project organizations as an opportunity for realizing sustainable innovation. This paper analyzes 30 worldwide cases of sustainably innovative building projects, concentrating on the cooperative learning behavior of actors that ignite and contribute to sustainable building innovation. The analysis sheds light on three clusters of project organizations, their typical cooperative learning processes, and the cooperative sustainable learning effects achieved. Theoretically, this work contributes by suggesting an analytical model to study learning forms that support sustainable building innovation; practically, this study indicates that cooperative learning practices facilitate professionals, regardless of their role in project organizations, to be innovators in proposing and achieving green innovation in the construction engineering sector.