M. Mavin De Silva

Perceived streetscapes (people’s visual evaluations of the surrounding street environment) influence how people respond to transportation infrastructure, yet their role in shaping cycling behavior remains underexplored. Here, we combine street-view imagery, spatial AI techniques, and causal inference to examine how neighborhood perceptions condition the effectiveness of bicycle infrastructure. We derive six types of perceptions from panoramic images and link them to built environment features. Leveraging the staggered rollout of protected and painted bike lanes in New York City, we implement a quasi-experimental, causal inferential framework using propensity score matching, difference-in-differences, and causal forest models on bikeshare origin–destination data. We find that bike lanes significantly increase ridership, with no detectable pretreatment effects. Protected lanes increase ridership by ∼32% (β=0.2705,p-value<0.001), while painted lanes increase ridership by ∼10% (β=0.0967,p-value<0.05). Treatment effects vary systematically across perceived environments: painted lanes perform best on already safe and lively streets, whereas protected lanes remain effective across a wider range of contexts and generate the strongest gains in stressful or lower-quality environments. Street-level counterfactual predictions provide the possibility to integrate human perception into infrastructure planning and design.

Transition to electric bike-sharing systems (EBSS) offers a sustainable and efficient solution for urban mobility, reducing emissions and enhancing accessibility. This study used data from New York City's bike-sharing program, combining socio-demographic factors, built environment indicators, and bike station placement insights to assess EBSS adoption. Using participatory system dynamics modelling, we developed causal loop diagrams to compare the dynamics of cycling adoption in two contrasting contexts: New York, USA, where EBSS is well-integrated as a last-mile and supplementary transportation mode, and Colombo, Sri Lanka, where e-bike adoption remains limited. Key feedback loops identified in New York emphasise the role of infrastructure investments, public transit integration, and safety measures in driving EBSS demand. These insights were adapted to Colombo, revealing potential strategies such as improving cycling infrastructure, integrating EBSS with transit systems, and launching awareness campaigns to overcome cultural and behavioural barriers. The findings provide a systematic framework for policymakers, urban planners, and stakeholders to accelerate EBSS adoption in diverse urban settings. By applying lessons from New York to Colombo, this study highlights actionable pathways to foster e-bike culture and sustainable urban mobility.

The building industry is strongly reliant on materials, which account for 55%-60% of its expenses. However, due to outdated and time-consuming approaches, inadequate inventory management prevails. This is where developing technologies such as Deep Learning (DL) might help uncover solutions. Surprisingly, very little scientific research on DL has been conducted for this purpose. As a result, this study looks into the prospect of automating construction warehouse management by employing CNN for object detection and counting. During the initial investigation, 23 studies out of 26 used Convolutional Neural Networks (CNN) for image processing and object detection. Secondly, a model was developed and compared its accuracy to that of human counting and discovered that the model outperformed people. Industry professionals were interviewed to discuss the findings. Industry professionals highlighted the advantages and disadvantages of using such an automated system in construction warehouses. In conclusion, this study shows that the CNN base model outperforms people in counting materials, and the proposed automated inventory management system has significant industry potential.

Robotic Process Automation (RPA), which automates repetitive, rule-based operations, is becoming a crucial component of today’s enterprises as they compete in more dynamic business contexts. This study intends to provide implications on the Benefits Realization Key Success Factors (BRKSFs) appropriate for RPA projects, given that between 30% and 50% of RPA initiatives fail. The methodology of this study comprises three stages: identify the main contributing BRKSFs, develop a hierarchical relationship model for BRKSFs, and provide real-world examples to show the usability of BRKSFs using two case studies. The results show that having a clear, well-defined, and unchanging process is the most important BRKSF because of its strong influence over other factors. Three factors, namely, aligning the objective of the RPA initiative with the organization’s strategic objectives, choosing the right process for automation, and change management, have lower driving powers but high dependence powers than other factors. The five factors that have both high driving and high dependence powers are: prioritizing the benefits that can be obtained through the RPA initiative, performing a feasibility study, assembling a cross-functional team, having a team leader and receiving support from top management. This study sheds light on the interdependencies between BRKSFs for academics and business professionals, enabling them to determine which variables need to be considered most for RPA initiatives.

The ready-mix concrete supply chain is highly disruptive due to its product perishability and Just-in-Time (JIT) production style. A lack of technology makes the ready-mix concrete (RMC) industry suffer from frequent production failures, ultimately causing high customer dissatisfaction and loss of revenues. In this paper, we propose the first-ever digital twin (DT) system in the RMC industry that can serve as a decision support tool to manage production risk efficiently and effectively via predictive maintenance. This study focuses on the feasibility of digital twins for the RMC industry in three main areas holistically: (1) the technical feasibility of the digital twin system for ready-mix concrete plant production risk management; (2) the business value of the proposed product to the construction industry; (3) the challenges of implementation in the real-world RMC industry. The proposed digital twin system consists of three main phases: (1) an IoT system to get the real-time production cycle times; (2) a digital twin operational working model with descriptive analytics; (3) an advanced analytical dashboard with predictive analytics to make predictive maintenance decisions. Our proposed digital twin solution can provide efficient and interpretable predictive maintenance insights in real time based on anomaly detection, production bottleneck identification, process disruption forecast and cycle time analysis. Finally, this study emphasizes that state-of-the-art solutions such as digital twins can effectively manage the production risks of ready-mix concrete plants by automatically detecting and predicting the bottlenecks without waiting until a production failure happens to react.