Intelligent Risk Identification and Mitigation in Green Construction: A Lifecycle Framework

The growing concern over greenhouse gas emissions has brought heightened global attention to sustainability, prompting many corporations to take on environmental responsibilities and advocate for ecological transformation. Among the sectors requiring urgent intervention, the construction industry known for its high energy consumption stands out as a significant contributor to global emissions. According to researchers, buildings use a large amount of energy and release a lot of greenhouse gases [1]. Since the number of people around the world keeps increasing, it is crucial to build energy-saving buildings for the support of a sustainable economy [2]. To support the aims of the Paris Agreement and reduce harm to the environment [3, 4], using eco-friendly technologies and ideas in construction is necessary.

Due to the climate crisis and the wish of many nations to become carbon neutral, different countries have passed policies promoting green building to help save energy and reduce emissions. Buildings are considered green when technology such as renewable energies and low-carbon systems is put in place. Developers plan these structures guided by sustainability rules, always ensuring safety, usefulness, and comfort, and reducing the harm they cause to the environment. Gilbert and others early on described a green building as one that minimizes resources and follows environmental ethics in its design [5]. Green buildings not only hold together safely, but they foster good health and new ideas. They can be located in residential, commercial and office premises and show how technology and sustainability intersect.

The philosophy behind green building is governed by some main principles. First of all, conservation of energy and reduction of carbon emissions is essential in reducing the environmental issues as well as improving on how resources are utilized [6, 7]. Second, green architecture is focused on recycling in design processes and the building materials, more sustainable resources, and developing strategies on waste handling throughout the whole construction process [8]. In addition, the development plans are supposed to protect the environment by protecting the ecosystems and minimizing their impact during constructions.

In this case, it is logical to enhance green buildings into smart buildings. Smart building concept has gained momentum due to development of computational and communication technologies which bring about smart buildings to act as smart environments. These structures use automated control systems, sensors and microcontrollers to control processes like lighting, HVAC and security. By doing this, they would make power consumption more efficient and a positive impact to the environment results [9]. Smart technologies such as predictive analytics and smart grid integration also enable the building to optimize the use of power and enhance the process of planning and energy distribution [10]. The AI integration also adds functionality in the form of ability to identify users and monitor the environment [11], further broadening the range of smart building applications.

Green-smart building (GSB) systems will ensure that the energy consumption and pollution are significantly cut down, human well-being is enhanced and the environment and the society live in harmony with one another. Such buildings serve as systems that are integrated both in terms of design, building, operation, maintenance and recycling. This paper starts with the discussion of fundamental principles of green building and how smart technologies upgrade every phase of building lifecycle. It is devoted to five interrelated spheres, such as design, materials, building, renovation and management, and assessment of structural damage. In every sphere, the implementation of such technologies as AI and IoT is addressed in detail.

The paper ends with a discussion of sustainability initiatives at all the phases of a lifecycle and gives a synthesized general overview of the technologies that will be used to enable them.

Identifying risks is a critical initial step in the lifecycle of green-smart building (GSB) projects. These projects, which integrate sustainability principles with intelligent technologies, inherently involve complex systems and interdependencies. Therefore, comprehensive risk identification is essential to ensure successful project outcomes, operational efficiency, and long-term sustainability.

Integrating both environmental and technological targets poorly at the design stage may result in risks. A bad choice or wrong placement of photovoltaic panels or wind turbines can cause them to work poorly or break apart over time. Not considering how the environment will affect the project site or unique climactic risks can put both sustainability and practical construction at risk. While construction is happening, several possible risks need to be taken into account. Sometimes, these problems appear when eco-friendly products are used incorrectly, energy-efficient construction is inconsistently followed, the arrival of key parts is postponed or no one is skilled enough to install smart upgrades. Also, if the team does not always supervise energy use or waste, the project may not uphold the standards for being called a green building.

During operation, important risks come from possible cybersecurity issues, problems relating to how sensor data is handled by the algorithms and system outages due to poor maintenance. Adding smart systems to buildings can make them more complex which without proper management might lead to wasting energy, privacy leaks or unhappy occupants.

The type of material used can bring certain dangers. If we use resources with big embodied energy or limited thermal effects, it may reduce our efforts to be sustainable. Misunderstanding how selected materials and smart systems could interfere with each other may also decrease the effectiveness of the automated systems.

A strong risk identification framework makes GSB more viable and helps deal with challenges at every stage of their development. Key risks that can appear at the various stages of GSB projects are shown in Figure 3.

Figure 3. Types of risks involved in design, building and running green-smart building (GSB)

3.1 Integrated risk identification and mitigation in green building design and construction

The early phases of GSB projects spanning both design and construction are critical for ensuring long-term sustainability, safety, and resilience. These stages concentrate a large share of risks, as they involve extensive material deployment, structural decisions, technological integration, and financial commitments. To secure reliable outcomes, risks must be systematically identified and mitigated through the application of intelligent technologies.

Natural hazard and climate-related risks are among the most significant. In seismic regions, inappropriate structural configurations heighten vulnerability to earthquake damage. To counter this, Calledda et al. [12] proposed seismic-resistant design strategies using network inversion and optimization, while Cetin et al. [13] demonstrated that Differential Evolution (DE)-optimized Tuned Mass Dampers (TMDs) reduce structural vibrations. Flood-related hazards have also been investigated with AI-based predictive models: Zabihi et al. [14] applied Support Vector Machines (SVMs) and multi-criteria frameworks for real-time monitoring, Wang et al. [15] combined Long Short-Term Memory (LSTM) models with the Analytic Hierarchy Process (AHP) to predict flood risks, and Hussain et al. [16] applied Convolutional Neural Networks (CNNs) for flood severity classification. For hurricane-prone regions, Bayesian networks and image analysis supported damage estimation [17], while Patel et al. [18] developed AI-enabled irrigation systems for resilient landscape management in arid climates.

Financial risks remain central to green building construction, largely due to limited historical data and uncertainty in ecological standards. Conventional cost estimation methods often underperform. Artificial Neural Networks (ANNs) have been shown to improve predictive accuracy in cost estimation [19, 20], and Alshboul et al. [21] integrated Deep Neural Networks (DNNs) with XGBoost to enhance financial forecasting for environmentally responsible projects. Multi-output regression models have been applied to construction cost prediction by jointly estimating multiple cost components, offering an alternative to single-output forecasting approaches [22]. Environmental and operational risks frequently arise during construction and early operation. HVAC system malfunctions or inconsistent energy use can degrade indoor air quality and energy efficiency. Zhu et al. [23] developed lightweight ANN-based ventilation models for energy-efficient air quality management. Wang et al. [24] combined life cycle assessment (LCA), BIM, and XGBoost to model and reduce carbon emissions during foundation construction, while BIM itself has been recognized as a collaborative platform for risk-aware decision-making [25]. Complementary approaches include deep learning–based models for operational monitoring and lightweight load-based ventilation models (LLVMs) for energy-efficient system optimization, as demonstrated in recent studies on deep reinforcement learning control of HVAC systems [26] and IoT/deep learning–enhanced building energy monitoring [27].

Design-stage risks introduce additional vulnerabilities. Poor placement of reinforcement or structural elements increases the likelihood of wind or seismic failure. Alanani and Elshaer [28] applied genetic algorithms for optimal shear wall placement, while Zhao et al. [29] employed Graph Neural Networks (GNNs) for adaptive beam configuration and real-time risk identification. Multi-objective optimization is commonly used to manage trade-offs related to occupant comfort and energy efficiency; Baumann and Kramer [30] demonstrated the effectiveness of evolutionary approaches for such optimization problems. Prefabrication is also a risk in terms of scheduling and logistics which is becoming more efficient and has less impact on the environment. Yin et al. [31] employed an advanced genetic algorithm to aim at the optimization of prefabrication and transportation schedules, and Sun [32] showed that digital twins, with the help of the BIM and AIoT, would allow predicting and preventing risks related to prefabricated parts.

Altogether, the risks that can be identified during the design and building stages include natural risk, financial risk, operational risks, and structural weaknesses. These risks can be identified and addressed systematically using AI-based prediction models, optimization algorithms, and BIM-based simulations and enhance the resilience and sustainability of GSB projects.

As Figure 4 shows, the categories of risks, including seismic, flood, financial, and environmental risks, are accurately mapped to intelligent mitigation techniques, including DE as a structural optimization algorithm, LSTM and CNN as predictive hazard detection algorithms, ANN and XGBoost as cost prediction algorithms, and BIM-enhanced ANN as environmental and operational efficiency algorithms. This visualization underscores the role of AI-driven technologies in supporting comprehensive risk management across the early phases of GSB projects.

Figure 4. Diagram linking risks to intelligent mitigation strategies in green construction projects

3.2 Risk identification and mitigation in building operation, management, and energy systems

The operation and management phases of GSB are increasingly characterized by the adoption of intelligent automation, advanced energy systems, and smart control technologies. These innovations enhance energy performance, sustainability, and user satisfaction, but they also introduce complex risks that must be identified and mitigated. The primary challenges fall into three categories: operational and monitoring risks, cyber-physical vulnerabilities, and energy cost control uncertainties. Together, these risks shape the resilience, efficiency, and long-term viability of green buildings. Table 1 summarizes the main categories of risks identified in building operation, management, and energy systems, along with the intelligent techniques applied in the literature to mitigate them.

Operational and monitoring risks arise from the reliance on smart meters, occupancy detectors, and temperature sensors. Device malfunctions or communication delays can compromise control accuracy and increase energy consumption, thereby undermining sustainability objectives [33]. Intelligent solutions have been developed to address these challenges. For instance, Gomaa et al. [34] applied AI-based systems to detect fire risks, forecast fire propagation, and optimize evacuation planning. Similarly, Kim et al. [35] employed CNNs with grayscale surveillance for anomaly detection in real time. Despite these advances, cyber-physical vulnerabilities remain a major concern. With the widespread integration of IoT, building management systems (BMS) face heightened risks of data breaches, unauthorized control, and network overload. Misconfiguration or interruptions in IoT-enabled control systems for air quality, lighting, and HVAC can compromise both safety and user comfort [36].

Electrical hazards also remain critical. Ren et al. [37] demonstrated the potential of Fuzzy Logic Reasoning (FLR) to identify low-voltage arc faults, while transfer learning and expert systems further improved fault classification and predictive maintenance reliability [38, 39]. Beyond technical hazards, user dissatisfaction and regulatory misalignment constitute reputational risks. Guo et al. [40] applied natural language processing (NLP) to social media data to identify patterns of occupant dissatisfaction in LEED-certified buildings, while Xiao et al. [41] used Latent Dirichlet Allocation (LDA) and Semantic Network Analysis (SNA) to reveal mismatches between national green building policies and user expectations.

Energy cost control represents another major dimension of risk. The building sector accounts for nearly 40% of global energy use and over 75% of electricity demand [42], with cooling requirements expected to triple by 2050 [43]. While smart technologies support predictive energy optimization, they are also sensitive to data inaccuracies, model drift, and integration failures. ANN–GA scheduling frameworks [44], deep reinforcement learning models [45], and temporal convolutional networks (TCNs) [46, 47] have demonstrated potential in load forecasting but remain vulnerable to sensor noise and behavioral shifts. Attention-based deep learning models provide higher accuracy but risk misinterpreting feature importance under unseen climatic extremes [48]. IoT-enabled controllers for hybrid renewable systems [49] improve dispatching but remain exposed to failures from data overload or network instability.

Risks are also evident in renewable energy subsystems. Rooftop solar designs face spatial estimation errors: Li et al. [50] applied U2-Net with GIS, while Ibnelouad et al. [51] leveraged Particle Swarm Optimization (PSO) to improve system sizing, though both approaches remain sensitive to parameter variability. Water and resource monitoring, as explored by Mohammed et al. [52], shows vulnerability to misclassification in arid climates using MLP and SVR. Wind energy management introduces additional risks: Vives [53] employed KNN and SVM for turbine fault diagnosis, while Zhang et al. [54] used LSTM-based variational autoencoders, which risk misdiagnosis under incomplete datasets. Integrated renewable systems compound these risks, as IoT-enabled hybrids combining SVM, RF, and ANN architectures remain vulnerable to synchronization failures and controller malfunctions [55-58].

In summary, the operation, management, and energy systems of GSB must address intertwined risks that include sensor reliability, cybersecurity, predictive accuracy, and renewable integration. Intelligent technologies such as CNNs, NLP, ANN–GA hybrids, reinforcement learning, BIM, and IoT-based controllers demonstrate strong potential for mitigation. However, their effectiveness depends on overcoming data scarcity, ensuring interoperability, and building trust through robust governance and policy alignment.

Table 1. Risk identification and mitigation in building operation, management, and energy systems

3.3 Material integrity, damage assessment, and waste management in green construction

Performance of the materials, structural integrity, and recovery of the damages are vital aspects of sustainable building. Other than environmental impact, construction materials and waste management plans define resilience, safety of operations, and adherence to sustainability goals. Smart technologies offer additional possibilities to assess, forecast, and reduce risks through the selection of materials, structural damage, and waste treatment after damage, but also create doubts that need to be thoroughly considered.

Advanced insulation, phase change materials (PCMs), and smart glazing have a high potential in the field of functional and adaptive materials to have impact on reducing the amount of energy demanded. However, such materials also have long-term threats in terms of reliability, fatigue and interaction with users. Hai et al. [63] demonstrated the use of ANNs to model the thermal behavior of PCM-based radiant heating systems, highlighting risks of phase instability over repeated heating cycles. Deliktaş and Şahinöz [64] applied fuzzy multi-criteria decision-making for insulation optimization, though mis-weighting of factors may result in poor thermal performance. Openings such as windows remain a critical source of inefficiency, responsible for up to 50% of thermal losses [65]. Aburas et al. [66] proposed dynamic coatings and smart windows for solar regulation, while Tien et al. [67] employed deep learning (DLIP) to evaluate manual window operation, underscoring behavioral uncertainties in automation.

Sustainable and recyclable materials, while reducing embodied energy, introduce risks of durability and compliance. May Tzuc et al. [68] evaluated vegetated concrete walls, noting risks of mold growth and structural degradation under moisture. Gogineni et al. [69] applied XGBoost to predict compressive strength in fly-ash concrete, showing variability linked to feedstock composition. Similarly, Xiong et al. [70] used KNN to assess fatigue in recycled steel, finding that external load variations compromise safety margins. ANN-GA systems have modeled acoustic absorption in recycled carpets [71], though issues of fire resistance and aging remain unresolved. Bio-based alternatives such as bamboo, rammed earth, and fungal composites present renewable solutions but face challenges in standardization and mechanical validation [72, 73].

Structural damage caused by environmental hazards introduces another layer of complexity. Earthquakes, floods, hurricanes, and tsunamis can compromise load-bearing systems or cause partial collapses. Termite infestations and biological degradation further weaken structural components [74]. Intelligent systems have been developed to support rapid assessment. Song et al. [75] combined genetic algorithms (GA), ANNs, and SVMs for real-time damage detection. Li and Tang [76] applied decision tree classifiers to multi-temporal remote sensing for seismic damage classification, while Oh et al. [77] designed an unsupervised CNN-based anomaly detection framework to identify deviations from baseline structural states. Deep residual networks (ResNet) have further improved hidden risk localization [78].

Equally important is the management of waste following demolition or natural disasters. Inefficient logistics and improper disposal of hazardous debris can exacerbate environmental impacts. Khan et al. [79] illustrated how recycled PET can be applied in construction, with ANN models predicting pavement performance risks. Elshaboury and Marzouk [80] optimized waste fleet operations with multi-objective models to reduce emissions and delays, while Na et al. [81] used transfer learning for real-time debris detection and classification. Additional research confirms the value of intelligent image recognition and optimization frameworks in reducing post-damage risks and improving resource recovery [82].

Overall, intelligent technologies support the assessment of material reliability, damage diagnostics, and sustainable waste management. However, risks linked to degradation, mechanical uncertainty, data dependency, and operational variability must be addressed to ensure resilience and environmental compliance in green construction.

3.4 Data governance and cybersecurity in AI-enabled green-smart buildings

The growth of the dependency on AI-based analytics, AIoT sensing, and Building Management Systems (BMS) in green-smart buildings requires effective data governance and cybersecurity mechanisms. The first step in risk mitigation is to minimize data meaning that only the data needed to achieve the required operational and safety goals should be collected and stored. A hybrid edge-cloud processing model can be suggested to increase the resilience and privacy of any architecture where the inputs that are latency-sensitive and those that are privacy-sensitive are processed at the edge, and long-term analytics and optimization are aided with cloud computing. Zero-trust segmentation should be embraced in cybersecurity architectures, to isolate subsystems in the building, with constant authentication of access to mitigate the spread of lateral attacks. Adaptive security management is supported by an incident response lifecycle, which consists of detection, containment, recovery, and post incident learning. The correspondence to the international standards goes even further to provide responsibilities: according to NIST Cybersecurity Framework (CSF) and IEC 62443, the asset owners establish governance and risk tolerance, operators establish secure configurations and monitoring, and technology vendors establish secure-by-design hardware, software, and update protocols. A combination of these measures offers a systematic way of ensuring the integrity of data, the accessibility of the system, and the confidence of the occupants in AI-enabled green-smart buildings.

Intelligent technology in green construction is opening up new paths for the construction sector to become more sustainable, strong, and efficient over time. According to this study, smart systems, varying from AI used for cost prediction to advanced tools for monitoring the environment, help solve the challenges of green building development.

It has been found that AI and ML [12, 13] play key roles in predicting and managing risks. By using TMD and optimization for earthquakes and LSTM networks and decision-making systems for floods [14, 15], they allow for forward planning and on-the-spot decision-making support. Also, using predictive technologies and optimizing energy resources both support greater building performance and a longer lifespan for major infrastructure elements.

Even so, a few challenges remain for these groups. To start, using past data and information from sensors may not allow self-driving cars to adapt as well. For instance, cost prediction models such as DNN or XGBoost require extensive, high-quality datasets to function effectively [21], which may not always be available, particularly in developing regions. Similarly, intelligent systems embedded in BMS must be carefully calibrated to local climatic and socio-economic conditions to avoid misalignment with user needs and sustainability goals.

The other problem is the issue of striking the right balance between innovation and feasibility. Although the advantages of prefabricated buildings and new materials in terms of sustainability are impressive, they have not yet been implemented on a large scale due to regulatory barriers, cost-plus, and issues with the supply chain [31, 32]. Moreover, to guarantee the safety and privacy of the gathered building and occupant data especially in IoT-based systems and analytics on the cloud, strong governance mechanisms are needed to ensure that they are not abused to compromise user confidence.

The role of interdisciplinary collaboration is also important in the framework of the discussion in terms of systems view. Planning of smart green buildings requires the collaboration of data scientists, policymakers, end users, architects, and engineers. Also, the policy incentives and green certification programs, including LEED v5 [83], may be required to facilitate the massive implementation of the intelligent and sustainable building practices.

Moving forward, a greater number of studies ought to be conducted on hybrid frameworks combinatorial of AI and domain-specific knowledge to bring about better interpretability and robustness. It is also important in the creation of open access datasets and benchmarking tools which allow for innovation and performance comparison of green construction projects among the internationally green construction projects. Lastly, the introduction of equity and inclusivity within the design of green-smart buildings will support the use of the sustainability and technology advantages such that all socio-economic groups are allowed to enjoy them.

To sum up, intelligent technologies provide a good way to overcome most of the structural and operational barriers of green construction. With the help of smart innovations and by aligning them with sustainability goals, the construction industry can go to the place where the future would become not only more efficient and resilient, but also more equitable and environmentally friendly.